{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5hIbr52I7Z7U"
   },
   "source": [
    "Deep Learning\n",
    "=============\n",
    "\n",
    "Assignment 1\n",
    "------------\n",
    "\n",
    "The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.\n",
    "\n",
    "This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "apJbCsBHl-2A"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import tarfile\n",
    "from IPython.display import display, Image\n",
    "from scipy import ndimage\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from six.moves import cPickle as pickle\n",
    "\n",
    "# Need to use matplotlib inline to produce plots inside jupyter notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jNWGtZaXn-5j"
   },
   "source": [
    "First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/mgalarny/Desktop/Python_Tutorials/TensorFlow/Udacity\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Middle'>The following cell will not run without internet connection. Be sure to acquire the files in another way</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186058,
     "status": "ok",
     "timestamp": 1444485672507,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "EYRJ4ICW6-da",
    "outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified notMNIST_large.tar.gz\n",
      "Found and verified notMNIST_small.tar.gz\n"
     ]
    }
   ],
   "source": [
    "url = 'http://commondatastorage.googleapis.com/books1000/'\n",
    "last_percent_reported = None\n",
    "\n",
    "def download_progress_hook(count, blockSize, totalSize):\n",
    "    \"\"\"A hook to report the progress of a download. This is mostly intended for users with\n",
    "    slow internet connections. Reports every 1% change in download progress.\n",
    "    \"\"\"\n",
    "    global last_percent_reported\n",
    "    percent = int(count * blockSize * 100 / totalSize)\n",
    "\n",
    "    if last_percent_reported != percent:\n",
    "        if percent % 5 == 0:\n",
    "            sys.stdout.write(\"%s%%\" % percent)\n",
    "            sys.stdout.flush()\n",
    "        else:\n",
    "            sys.stdout.write(\".\")\n",
    "            sys.stdout.flush()\n",
    "        last_percent_reported = percent\n",
    "        \n",
    "def maybe_download(filename, expected_bytes, force=False):\n",
    "    \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "    if force or not os.path.exists(filename):\n",
    "        print('Attempting to download:', filename) \n",
    "        filename, _ = urlretrieve(url + filename, filename, reporthook=download_progress_hook)\n",
    "        print('\\nDownload Complete!')\n",
    "    statinfo = os.stat(filename)\n",
    "    if statinfo.st_size == expected_bytes:\n",
    "        print('Found and verified', filename)\n",
    "    else:\n",
    "        raise Exception('Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "    return filename\n",
    "\n",
    "train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)\n",
    "test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cC3p0oEyF8QT"
   },
   "source": [
    "Extract the dataset from the compressed .tar.gz file.\n",
    "This should give you a set of directories, labelled A through J."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 186055,
     "status": "ok",
     "timestamp": 1444485672525,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "H8CBE-WZ8nmj",
    "outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large already present - Skipping extraction of notMNIST_large.tar.gz.\n",
      "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n",
      "notMNIST_small already present - Skipping extraction of notMNIST_small.tar.gz.\n",
      "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "np.random.seed(133)\n",
    "\n",
    "def maybe_extract(filename, force=False):\n",
    "    root = os.path.splitext(os.path.splitext(filename)[0])[0]  # remove .tar.gz\n",
    "    if os.path.isdir(root) and not force:\n",
    "    # You may override by setting force=True.\n",
    "        print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
    "    else:\n",
    "        print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
    "        tar = tarfile.open(filename)\n",
    "        sys.stdout.flush()\n",
    "        tar.extractall()\n",
    "        tar.close()\n",
    "    data_folders = [os.path.join(root, d) for d in sorted(os.listdir(root))\n",
    "        if os.path.isdir(os.path.join(root, d))]\n",
    "    if len(data_folders) != num_classes:\n",
    "        raise Exception(\n",
    "            'Expected %d folders, one per class. Found %d instead.' % (\n",
    "            num_classes, len(data_folders)))\n",
    "    print(data_folders)\n",
    "    return data_folders\n",
    "  \n",
    "train_folders = maybe_extract(train_filename)\n",
    "test_folders = maybe_extract(test_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4riXK3IoHgx6"
   },
   "source": [
    "---\n",
    "Problem 1\n",
    "---------\n",
    "\n",
    "Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import hashlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def disp_samples(data_folders, sample_size):\n",
    "    for folder in data_folders:\n",
    "        print(folder)\n",
    "        image_files = os.listdir(folder)\n",
    "        image_sample = random.sample(image_files, sample_size)\n",
    "        for image in image_sample:\n",
    "            image_file = os.path.join(folder, image)\n",
    "            i = Image(filename=image_file)\n",
    "            display(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/B\n"
     ]
    },
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/C\n"
     ]
    },
    {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/D\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/E\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/F\n"
     ]
    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/G\n"
     ]
    },
    {
     "data": {
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    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/H\n"
     ]
    },
    {
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     "name": "stdout",
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     "text": [
      "notMNIST_large/I\n"
     ]
    },
    {
     "data": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/J\n"
     ]
    },
    {
     "data": {
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   ],
   "source": [
    "disp_samples(train_folders, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/A\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/B\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/C\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/D\n"
     ]
    },
    {
     "data": {
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     "text": [
      "notMNIST_small/E\n"
     ]
    },
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    {
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     "text": [
      "notMNIST_small/F\n"
     ]
    },
    {
     "data": {
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     "text": [
      "notMNIST_small/G\n"
     ]
    },
    {
     "data": {
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     "text": [
      "notMNIST_small/H\n"
     ]
    },
    {
     "data": {
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    {
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     "text": [
      "notMNIST_small/I\n"
     ]
    },
    {
     "data": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_small/J\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_samples(test_folders, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PBdkjESPK8tw"
   },
   "source": [
    "Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.\n",
    "\n",
    "We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road. \n",
    "\n",
    "A few images might not be readable, we'll just skip them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 30
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 399874,
     "status": "ok",
     "timestamp": 1444485886378,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "h7q0XhG3MJdf",
    "outputId": "92c391bb-86ff-431d-9ada-315568a19e59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A.pickle already present - Skipping pickling.\n",
      "notMNIST_large/B.pickle already present - Skipping pickling.\n",
      "notMNIST_large/C.pickle already present - Skipping pickling.\n",
      "notMNIST_large/D.pickle already present - Skipping pickling.\n",
      "notMNIST_large/E.pickle already present - Skipping pickling.\n",
      "notMNIST_large/F.pickle already present - Skipping pickling.\n",
      "notMNIST_large/G.pickle already present - Skipping pickling.\n",
      "notMNIST_large/H.pickle already present - Skipping pickling.\n",
      "notMNIST_large/I.pickle already present - Skipping pickling.\n",
      "notMNIST_large/J.pickle already present - Skipping pickling.\n",
      "notMNIST_small/A.pickle already present - Skipping pickling.\n",
      "notMNIST_small/B.pickle already present - Skipping pickling.\n",
      "notMNIST_small/C.pickle already present - Skipping pickling.\n",
      "notMNIST_small/D.pickle already present - Skipping pickling.\n",
      "notMNIST_small/E.pickle already present - Skipping pickling.\n",
      "notMNIST_small/F.pickle already present - Skipping pickling.\n",
      "notMNIST_small/G.pickle already present - Skipping pickling.\n",
      "notMNIST_small/H.pickle already present - Skipping pickling.\n",
      "notMNIST_small/I.pickle already present - Skipping pickling.\n",
      "notMNIST_small/J.pickle already present - Skipping pickling.\n"
     ]
    }
   ],
   "source": [
    "image_size = 28  # Pixel width and height.\n",
    "pixel_depth = 255.0  # Number of levels per pixel.\n",
    "\n",
    "def load_letter(folder, min_num_images):\n",
    "    \"\"\"Load the data for a single letter label.\"\"\"\n",
    "    image_files = os.listdir(folder)\n",
    "    dataset = np.ndarray(shape=(len(image_files), image_size, image_size),\n",
    "                         dtype=np.float32)\n",
    "    print(folder)\n",
    "    num_images = 0\n",
    "    for image in image_files:\n",
    "        image_file = os.path.join(folder, image)\n",
    "        try:\n",
    "            image_data = (ndimage.imread(image_file).astype(float) - \n",
    "                    pixel_depth / 2) / pixel_depth\n",
    "            if image_data.shape != (image_size, image_size):\n",
    "                raise Exception('Unexpected image shape: %s' % str(image_data.shape))\n",
    "            dataset[num_images, :, :] = image_data\n",
    "            num_images = num_images + 1\n",
    "        except IOError as e:\n",
    "            print('Could not read:', image_file, ':', e, '- it\\'s ok, skipping.')\n",
    "    \n",
    "    dataset = dataset[0:num_images, :, :]\n",
    "    if num_images < min_num_images:\n",
    "        raise Exception('Many fewer images than expected: %d < %d' %\n",
    "                    (num_images, min_num_images))\n",
    "    \n",
    "    print('Full dataset tensor:', dataset.shape)\n",
    "    print('Mean:', np.mean(dataset))\n",
    "    print('Standard deviation:', np.std(dataset))\n",
    "    return dataset\n",
    "        \n",
    "def maybe_pickle(data_folders, min_num_images_per_class, force=False):\n",
    "    dataset_names = []\n",
    "    for folder in data_folders:\n",
    "        set_filename = folder + '.pickle'\n",
    "        dataset_names.append(set_filename)\n",
    "        if os.path.exists(set_filename) and not force:\n",
    "            # You may override by setting force=True.\n",
    "            print('%s already present - Skipping pickling.' % set_filename)\n",
    "        else:\n",
    "            print('Pickling %s.' % set_filename)\n",
    "            dataset = load_letter(folder, min_num_images_per_class)\n",
    "            try:\n",
    "                with open(set_filename, 'wb') as f:\n",
    "                    pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)\n",
    "            except Exception as e:\n",
    "                print('Unable to save data to', set_filename, ':', e)\n",
    "    return dataset_names\n",
    "\n",
    "train_datasets = maybe_pickle(train_folders, 45000)\n",
    "test_datasets = maybe_pickle(test_folders, 1800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"left\"> Display Sample Image </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ipython_display_samples(folders):\n",
    "    print (folders)\n",
    "    for i in folders:\n",
    "        sample = np.random.choice(os.listdir(i), 1)[0]\n",
    "        display(Image(os.path.join(i, sample)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['notMNIST_large/A', 'notMNIST_large/B', 'notMNIST_large/C', 'notMNIST_large/D', 'notMNIST_large/E', 'notMNIST_large/F', 'notMNIST_large/G', 'notMNIST_large/H', 'notMNIST_large/I', 'notMNIST_large/J']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABmUlEQVR4nG2SMW+OURTHf/c+t9VF\nKAnCRGhikQibiYGIkurbrZ9Awmo28ElUIphE4gsYBIvEZGCQlHagYqjX89zzMzzv2/K2Z7v3f87v\n/M/JgT4aBlbVzhUy/0di7xerWv06S+o/x0k2v54SQI4jF3eUZs7XUG1doZkQSfn1iLu2f8TdBuR4\nhECOQ5fSZGnm2E9DbWMXbuZJtD13yy9AKqWUmamFftTqImWyFD5Z//WbgByn7pgwfzhxtzZgWj/9\nPdnnF+6p6sPj1Z47oBmNUtPlbth1w/ry8xsqoDe3Xc4NDcPNuXTLTg3Xx34Lt+20+q5w+IfRc1MD\nGYJ5AHnVTa+9sALBoo42s2Fo9QbT6YpVDdcOkICGZTsNN46S08xHq9o5SA0ZuK4QvF9NkX8/RkAW\nBRL7vlm19X6ZKdPlTI3Y8luYt2pEnOsne26nVpdSKXLNyCTiwWaS7Elyv4dnAXv6bU9EuD4LXIiR\n1rVt27bt+OUgZa6m6Js1pZRSyuhwZEHK292oGq4e5Oyf3SQ1XM7zU7HzIgDCpb+87F/rYwYuuAAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABB0lEQVR4nH3Ru0oEQRAF0FvVM8YG\n4isyMDISs80FExMFA1PBWKP1C1T8AX9ARDBR0HizxUBFhRUDXyz4YEVwAgXd7e5rKDjVU+mhuql7\nwb8JrGGJve/itrE+CVGghIEk+bM/DlUYwxiyhZOpaKKo097AzqCJAJD7idUkwmEujYKxNBJvafQ4\nyBIUQ97aNJCMUMmv5jvGs6JZpo9rtQctb7Jzc986v/gUjWWML+3Lxl1XNNjBR75ujcDB+DOGyKH6\n2WxwBqpzQj96tBgSd6oGt32dTMj5/pWqVqarWhmuaqWowtMq3E0hu+5wz6oMpKCvuWzFJ6KqxcbM\n+7/KCODLfzy1m8fPKvEXrCqNz5Ow7xUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA1UlEQVR4nLWSv0oCUBSHf+fcAylh\nLjZIg05KL9Aq+Qo+RgTtQeBzNDY0BI3O0ivUFoKEm62B4D3n19Ki3tvmN56P85eDBHTuV8y+S+bi\n5kx9OLarCybdA/3rU3u47bxO+m0X7ED7XgoY3DRRQsVc4YF06BiwBCTAy7nF6PFleRoAmnNpjz8Y\nfK45e5SnN2Gl7GA4Oq84gMFPq0nP3JzUeioQtUTFP4c41m0DqK5pGgKpvAneyXWjXFTE7i6nX4NW\n+TUh6M14QNBfuipJth9reOyB5fznFyzieJh1vzYSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABcUlEQVR4nHWTMWsUURRGz71vJoXK\nZlItIkLSrKAY9geILjZibEWwtExhoQhqwMIy+AsUkbSSQrCVQKKNlRDQaFSshKSJccwq6yTzPotV\nspE3tz2cW3zfvVa2AECAsbPxZmVlPZrXANbVsSft6P1LZUY+cfLc+cOD1wtPByEKIPBQlcoWBhjF\n3A/p/QWCAT7GnCqVBcFDyIIx/Um17uMAGbf0+58JntPZUKUHOJBxYxRCzlXt7WqWkIA4L7Wr7cnh\n5v/GWMD2iptJKJZ3gnPlaApG//IWqX0xBTE+QqSXhPABGacazG0wjjeYfTDGG+Aw9QZ4BOBnEooC\nxGaDeQKLrDWYHTBepaDHydOYf3+eDv7seB159jkd/DWU9edx2C9yOHm83KvhznqAjNujZVvO1FdV\nmsfBx7i3f2AhOJ13quPdYdOBR6Oneej6lrTaIxhkXdozBLxbZgqtzpmZYrD8eLEKUWAH3kG/vq0u\nvVjT33f4A7d5mVhZacdLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAApUlEQVR4nJ1SOw5CMQxzSAcYQJyE\nizCxsnOIdz5GBiTEQRBiAhoz8ZoUtRLPUywrPydgA8bzYoYOumIKzEpIZSW6OsQ8iJT9Q0cmtyfc\ngJmrXk9FyQStHsiPVGUS9FQCu7zlG+bl4RgzNyXMum6bYGo/PUP7ehWHVDm0u4+c6YSpDhmjmJE9\n/ePYoSysW9aLgq07NuQajfegDE1vQX01HwyETH/qD5IYZCzrFG8ZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABYklEQVR4nHWSvUpcURSFv33ONRrx\nBwXBVxAi2MwrCKZP2kC6PEKwMYUPkCptqjQWIUEbixRpFERiE0wnWAQnBBWdOH/37GVxZ8y9d8av\nOM1irXX2ZhsQJHu2vra6NDfDA6n18x0QmXj1rS1J8iGSXKcQaJzIlbq9XBWSW0wvPz5NGAFSzweh\n0wbI2PiapSjj+MuP3/96APjk4YIMWDxVrqTmC/v/GSaa6uV5zhv15bpsEGMMAzLeKyXt8FlJuTaZ\nLDmNqdefdt/Ocy53tVcJlLHiuZVcl3NYVcxCiJbNAHgHVUTlQC1tSMgMIBsruhelAkhnldT+xd6H\ntgk0jlzbxMc6YRY9dPYrSv/P/hYqOmVXa91yhN/eDSaSXH+f1FJjeRSrbShRElXbUEEYOoM9Kgof\nM1UmA8Jyp3U3Jrg4yeuri6Pno96Oe3GI+jVf+zLhwFT4uWmPxK7sNruS1PreGIm9BzW629duU9mw\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABFklEQVR4nKWSvy5EURDGf3PuXYVs\nIqLyp9iIaDf7AioRJV7DY3iAjUrJE+j3CYRSdESn0FhBQe458yl2z7XcG5H4yvnNzDkz8xEIu2fX\n40o/FTUsfeNkWwZyvhQMgHJz1HOZQUFD5VGv6oDs6e49x6waLMoAniUp6WIVqwsYKSpqWC4AeDh+\nKJVhES1nTTSGVLclkUiFZwh1IRAoCijLFiQu516Dd2+QJFXaaR2lGQLMANQOpUZl8K3T2SV+g0Z3\n/Q9vtrQVb/cOWFqbz0mNUSa7lRRa2+Yf/hvab7AV51GWZkxUHzu8TOoPl6OnrHxAO9+fGuz2o36g\nNlj/UTEmd2+4WoLBlVySxxlNMy14Z++gv9JtMQKfBwi1cVTN/eUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACQ0lEQVR4nE2STUhUYRSGn/N9914d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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA00lEQVR4nO2TPWpCURCFz/w8G9HS\n9KIgWFho6QoshJQp3EFWmSJVBEGwEWwUREnAxkZ9c1I8jfeBC0jhaW7xzZ3TfCOt5RmiIkhDBpG9\nusAUIFNKqCKgUh3VXpr9bqX08zT/Wu0OnwAAsf6MSaYDvS4yd1MMDoyCBH/aUHOzW1OGzR2ucS3R\n4gkw7cxL8HGe8B9DQyKY4E8Cd9dUE/I4hoq5FxNiw8WdBfeTGgSA1N+yRrPX8VIZt7Pl5vsDbeYk\nI1I185xk8N2Ji0K0dA4KRrBy+QXB0H4U+5v+4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABqUlEQVR4nGVSwU5TURScc8pTFAjB\nwCsb4wKj/oHRxARISuISomu/QRPYaPQXIKyNblzJlp1NrCaugYWJhoaWB1FqxdTaxva+e8ZFKc/e\nzupOJmdO5p4B4rVStd2qFB/HEEAw86RYabUPS6sxsFSlJ0nP6iJEsHjOKwUc03kzM+9YjoConPEj\n3UEO5r0hhz0HuL2M72Jys0kjaY2NCYhgfKNhJI3NzUkRxvNzeat9/VAXAhDOzN+M9WS/WBOo4gy9\nl0Z9PqICqAgAGgEAwonbN2ZR2//0WzAIwcVnSS/Kt+ejCPGa3qfOpd74JtSihJ3Um/n0r9WGbFfq\nNJI0/ng4tJRXHy1dm5LT5N2raihixBPReK7TMmggCogLlyL3x0M1HZpceHB3eqr762B7K5Fg7vJL\n9uB5uhzkwFM6572ZT7v8OaeDchM5GEhS0OiGSV50erbGz3fw31JRgdmt+/dmr3TrX7ZLzUxTDU00\nmxROL1yPcVL++F0I1f4JAQjG1htndVkfQ5h+i9Y/1dvwY6JKVsmDaFATFI76ZU4Koa0gv1ZK2q3D\n96v5c+0fpUgXP71SwDkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train folders\n",
    "ipython_display_samples(train_folders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['notMNIST_small/A', 'notMNIST_small/B', 'notMNIST_small/C', 'notMNIST_small/D', 'notMNIST_small/E', 'notMNIST_small/F', 'notMNIST_small/G', 'notMNIST_small/H', 'notMNIST_small/I', 'notMNIST_small/J']\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABtUlEQVR4nGWSv2tUQRSFv5k3q4k/\nogmRxMJVRIMxhA2C1mJlYyHRTqJNFiuLSCrxTwjYWVqnsJEgqGgTQbAwha0INlqtkn0vhLw3M8fi\nvXE38Xb3fpx7z9w7sC8eKqiJqO/72dEfigO4ZYeQZaWNGeTbQ9DEU8sIiKoL+RBssXomGsCa/5RZ\nObcIwN6zr0QQ/eGZS+cri+g9H6vzAcxCp0sGsHn8rLf7YeDBybr2dvagW8PlR9EB8HEBw4GZT60B\novnQu0pd7f8TXtuVJJXqHt6RJHldb6BjQ0FS1F6nrQZeSexWriip0kt7p4ZBsxbABJaPeQMyvI4d\nAGS8b4S3FaIkry+nWVeUFPXnnAWMH+siAxje/eJS46Pw9dNu3KxagOzv9xPj003bvG47upVuHHbL\nKvn5POnA6O6CmivZkbQTkVcOcE9SxQM2La3wDudXZtLncIP7idw76yfvES0g03/8c7S/Nh9tUrqy\nOx8ygODWXwQbd1CtLIItL95v+hhehSNxbjrNKLAszpQZQMw2P1ExdSLBHHthlVbj4U2vBe3xZKjA\nLU0UtXDk2wYxY4rcAZSHtvkLHs7uyDih3fIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACFklEQVR4nF2RzYvOURiGr+c5Z7zy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\nb8iyTeXfXCePAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABsklEQVR4nH3Su2uTURjH8e/znDdB\nUElJQSzVeJsEsWDsIg5RqKLg4iYIXfwfnHRQECEugpNb7CY6OmiKFBcv4AWX6uKlqRcsFuutSd6c\n9+eQaGlJ/PFM58N5Duc5xwBwi4wcruzZWsy3lxqzM9MNnAwAgjNeW5CkLEqSFm8exLxrjNyIylL9\nTSeTbm8jGCSdytSWGHM2P/Ow8TVfKk/sJvXw5XQdAieaSlO9ObMRAyA59kRpM+oUzv5f6kRNDWEh\nCSGExAiXpGZtL6x/pjTqGpZ094ElgfP1MQzOqh1VN3dWYu6OO4W3imqVCayKewAmlXVUx+kTr5DB\nPawvlnHxrh+BlzD4PgA39F/vYud/uIAG42sEyQB8jGDzALyzHGB8UOO7SjU3vHYKHhzgiGKqC+RW\nG+COU1NL7aPkVsbrOSav78Qwis/V1tJxLAlmYCFxTi7rx6194Ox4pVaM1SGMbhWqMS7rQxlwRh+o\nk+rzlUOb8ljxwOWPaqd6NNo7PTn3U5mkhdmnLz5JyvT74rreG5uzvfr+37fNNH91F+6965lnKlQm\nxkrD+fa3uZfT9xcJmfgD847MpZY1ij0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABoUlEQVR4nHXSTUhUYRTG8f85545y\nc3IsaYaiEKpNteoDIQgjq0Vggn0xIAQV0ib6gCiiXauioGhlmzYRYUULF2YUrRo3Bn2Iq4lIhMQK\nrG56Vea+b4tBCLr32R1+8GyeIz3HyrffNdeK1yZfjv1iZamYfzb4B0uo58T4Vhh63Vq/iqc/V8+v\nwgCwgAcV2+j3kjMzE2h5PvSwvKTWeZQNyTZRACQgfHJo9BwBBHgXFUDxAoCvWXyn78DE/D1LFED4\nN4lVdO3h/i2JKimRgd7h0ccBaej8SKtc3dzl0tDLzI81r6aupNcq021+sH19KsLP5bzx+zMwChmT\nnRkYh8z4UgYu5JiPsjBQ1BYysDmmselrBjYtss5PZGBLLDvkRQbmv/nuqJKKzgrVUsfA7zQUv1qj\nM3YjdRWV9o+Nl+5/stRav+fp3emz6oL/yZKO6r5T22fVK6IqLLpwqSRIlh3h1u4P6lD1rmtSv091\nJ07NVKiF/Zs6eyrmAOTydeBk3JsDYEX57fjxhvrbysGLu27OWpK7kH8/8mWu0NCWfzQ8hzqAv33H\nhpMw5SO3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABTklEQVR4nJ2SsU5WQRSEv9k99xdI\nDPbGwpD8BQmdwYaHsOYhrHgDH8OCd/hrY2diKx2gsTRBAppAUO7ujsW9xnuJUniqPefMzMlkh7eu\n/msVrxL31L3LaK0hjV2bU2I9Tci/nx7QcfLIevJw7E57ADYfT7RfuNoufj3Kb75zcfEqSVnHCDAf\n3ElS9/09Bghjd6PCQgZsxWA1gAEGkFIDcFZH0MXU122tA/bbWR81zgWp7RxZYH36vHCNN6/y2kaD\n9HPCFFtbABfo+nrwOZWtDUpcQcoAni1zho41aO0u07q8CEp8oe7tN6EzILHjZrv3gTYWiweReWnb\n/jqTLf7RgOCm1Gydz5aJbDCkULYiBhPjVWwmgxBS/eNVgBgHYSrPsEDsqgfoeT5CD3dbero+fvZJ\nGfwuBdbHWG5PjiyZVdy0lv4ZsJQm8bwT1P8P9S8gDZ+0ltseqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABK0lEQVR4nH2SvU4CURCFv1kvLgQF\ndrHR2PkcPo6tnaUxsbCysbGz83VMfAkNySKoAY3sZY8F7LI/hFPec+ecOTMD5o6/lamCTKPYGQ75\nsWggmwDOuldx2CR7D8n9Hwy0Hb8dgmg49Vtk5SfDgSVBZICWJcYBZNO5banKsXDCQDZ6tEKxe9mR\ngRZIkpZ6KVXsf6xyz1xu42q+hTcgbwrveoAF7S5rj7XsK0avlrSQBVg8R0DrvLWXN1dU5ugk+Rpm\nQSNd2mwITOFNX6b2AZvIm4YOf2oNlWU19qn3vilrzuSck5U+F6T38JnVJyQMjJNrU9ivUNLulY1L\nyy6Pi2w6JzqbpLXDlKQsfTsd7DwwZ/42vgirEZDNn5LVW+urefHvAAHmjqplAASxM/4BfSsNgaPH\nS9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACUUlEQVR4nDWSS0iUYRiFn+/7Zkal\n8gIWYdldRyyQbETtQthl0cKCWqRC0aYWhS1ShEq7SG1MdC9EVLQwXYiFRkVhpSJhWbYoIhKlWiRk\nicyMzvynxcy828NzzuHlGACsrLe8vjQnt791yRcXYETyLGCWNS5JGtqBsZmbHSapGQoHWi1seSxp\nsdkRKKjJT6rO7IpKwyHg7B/F9CYE2aGslGt+l6Tw5QAUDSiucLMfAgnND1R/lZeAz/1VTKNlqcC+\n21mQ2yVpvikAwSfyNH8p2xjgqPTpAHDkm6TRijTM+TlJ7RgwtfOS15kJq+51Vx+qCPgg+Oh2ZUbC\ntuSlpMn9qXbbQsku1mHANi5I6sh0zucs5AB+B2BaozehpLOKWMGUleXw1tmAG3pnrKvL56kGi8F3\nIfYCC/iK+iRFLln8la8Y9zRXb2HnKRxr9gHHphTX61KAhrDiGixK/L94qTMLVt+XtHDRD5SNKqa5\neou1FC3pQxVQM6O4hsqB9CsReRoM4iP/n6SOTMh7IGmYLKB8TDHNhQgQejb5PgHXTquN7T2bIP1q\nRN5YBgZcreS1r4C1vdWY8dkTQMWY9LzUgguNyNPHKsAHx6WH6yHtWlTRibuF4G+JSF7bCgsm44v0\n6yRQMSJp9gxQNqy4htMMjoNRST3rIf16RJ76CyDQHFYjDix7Pyuqn8nkuH6fBna3gAEcOXckqXsd\nZLQuylNv0E9qmw7qfkiaqQMq30qayHW+1KiNJe/WtKT+oCP9xnhTnjWJISTgOCv37N6w8XvDTAzr\nJaH/ZMsggpZQUr4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAB5ElEQVR4nDWRX05TQRSHvzl3ANEI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\npFgcF48x/gO5+ufJtpLnMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABbklEQVR4nG2RP0uVYRiHf/f9nFcS\nz9EGCRrMwKWWSFpK0CH6Bi4aNCpELS19gaAv0QeIoEGIoqHIP+ii4BDUGDRZCDqkHjjPe18Ndeqc\n9/HeHi6u33P/MUmSYc+WTsYuddSvXz/O+Np/mKsan350ChB8fzAz0VKzXlED+Y5Mkg0SU7VPEGyl\n5GbDmulWBjIP/0o+AF3LCZHyphqaZKZ9anq8GS1N5+6MTOjjWYsC6l4nW1SHO6JI1fg6mR4f5KaG\n6bo5jyvpk5wmRHOeDTt+rbpM7exSE2yXc8h0g4Dg8X840O2iJFm8LTzJ9Jkg8+5CaTq3r0oKrXUT\nTZi02A6L6mjvvNDWFpkeazbQrPfFhVm5TNtUNKE0N9azSAfvz9vAxAaZmnWl4kvXLBHAk6Hr982n\nZKA7VezO3KovBMFGGoJuEmL1uiT0rW7eObWvrBwRAIf3p9rVv1vLXl6rRyYvn2Zh5iPp53H34ovn\n9sf/DbSBqh0k7mwtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAzklEQVR4nO3RIU4DURSF4f/cmXSa\nClQFAsMaAIeuYUtIkm6AlaBBkFSgSDUeCclgypS+dxAzCW3yOivgqpd8Ofee5EFhxMXqs329KRnB\ns1P2exHFm5NTjqIO+RFEYzia/EeAWgHZR5AEUlnr6dVZ+/JxRB+883p+eLv/bBvntPUlVQnrHKWV\n6m9G/67wH8SPh7YAFSnt5fJQobYIlvdtt9mRCWIymzWLcwIsZzSc2BuDiXgKJA462ZZEPOr09vqk\nmU4aqV9gbHfd9/ZrdfcLWTVSGyxjs0UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test folders\n",
    "ipython_display_samples(test_folders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vUdbskYE2d87"
   },
   "source": [
    "---\n",
    "Problem 2\n",
    "---------\n",
    "\n",
    "Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def matplotlib_pyplot_samples(pkls):\n",
    "    \n",
    "    for idx, pkl in enumerate(pkls):\n",
    "        with open(pkl, 'r') as f:\n",
    "            dataset = pickle.load(f)\n",
    "    \n",
    "        sample = np.random.choice(len(dataset), 5)\n",
    "        plt.figure(idx+1)\n",
    "        \n",
    "        for i, k in enumerate(sample):\n",
    "            plt.subplot(1,5,i+1)\n",
    "            plt.imshow(dataset[k])       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UcRw3wHHcvQC+C+AqjuN4ADun/593mM1mdHd3Y2hoCDabjUUTEoE1Gg1zaaKc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b5c950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5YsYzVSihLDCl0+mkgFq6dCnXX389r732GmlpaUpej4smolmHaKbi9/vjEuRd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\nDow4qM1mM2azGb/fT3d3t0zqEu3W3n3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a123750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lyxa2b9+OzWaT29T5gtDLOhwOzpw5Q3V1Nf/+7/8uMxtGo1G0Wi0rV67k9ttv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\nublZGNw+oYYmwhNBhJD7fL4pqoy5Mhr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6muXLl5OWlkZWVtaUXOfCiCcCfs6ePUtzczPd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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115828e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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OXjGchEOMCY1GIyNyhTBXap5Kj5/BwUGampouCCfiGYgU2CIhVaR4AqUcCB/j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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154625d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mlawgFwF8zPN8AMCLgiBsSTK/qCCjJyvII83kdTqdrCCPdI1cHpF+MxqNmDBhAtUts/bU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\nRgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b98b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ETUm0ztF4pIzFGKgmpshOSZKKZVnuGHu4DfibmkrmemRnd3d3YEom1boEfo5Q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\nkuAENZpNucyUzMglSdIDjwA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c21b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s1BxnKuKZ29vbGRwclCYWZVx3c3NzVOaY2lsuGL2pqWlaDXKlqSU9PZ2HHnqI\nN954gxtuuIHc3FxaWlqoq6tjeHgYn88XZZcGZJz1fCCuJ7bdzc3NnDp1Ku6WSj0GEfNeXV0ty9iK\nqIprhZm89rGOmyv8fj92u51z587R3d0dlfCjNCsJc9OqVatYt24d69ato7i4mOTk5GlC9A+Ba3lf\nsbg1NDTI0hECsSqAZmRkUFpaSlpa2kf+/KKCoqixbTQaGR0dVYZdXlPU19dz5swZuWNX79YE1D4l\nm81GaWkp5eXllJSUkJ2dHZUO/4fiG4GkpCSys7PnX8bW4XD8ELgX6HU6nauufJcB/BpYBLQCu5xO\npzfuRa5AxESKxgbqeOh4L1hsj/x+f9x64uFwWAplg8Egi9Aoi9MAUTVT1NtNnU4nU/4//PBDfD4f\nubm51NbW0tPTI3cAOp2O3NxcrFYr9913n9zy6/V6CgoKMJlMOByO/VrootSOJicnCYVCDAwM0NbW\nRmNjIwMDAwlpKmC326moqKCyslLWgBAMG8+mrBXxtpbq9/X1r3+dAwcOkJWVxb59+4Apej/33HN0\ndXXR2tqqiSZjY2N4vV4KCgq4dOkSHo9HFuxXjkX5ef369axYsYKioiJSU1PjalLXe2Kqr//888/z\nwQcfzJsmMBXRYbfbpc9GzCdl2KHy/larVca4JxrjtYLyusLslpOTQ3l5OampqaSkpGAymfjHf/xH\njh49SlZWltDAJU06OztnRROhHApfkrrswUyLRlpaGhUVFZSXl5OVlTUtqfCjEuTx7qM0VyaClqDS\nHwN3qL77GvDOlSpl7wH/j4bryDomohuHsPtq9fomslUJpi0pKWHJkiVySye0UnGcOn5Yqa1HIhFp\nc7xw4QK/+93v+NWvfsWZM2dkRxBxnrDJKa+ZlZUla17Mhi4C4+PjeL1eent76e7upqurS5pyYHr8\nq3Lxs9lsLF68mMWLF8vY+uuh+Sgh6Cd+HnjgAX7wgx9EHfPiiy+yefNm9u/fDxppEgqF5IRUTk7l\nTmtychKDwSBNPZs3b6aysjKmOekPifvvv58f/vCHUd8Jmrz55pswCz4R2YmDg4OEw2FpVlFqmEph\nLswGsWqqfBTQ6XQUFhayfv16Kisro5QfNZ/853/+J5s2bZoVnwDSz9ba2kpzc3NUueKZtFiY2sk6\nHA7Kyso+snlzPTCjRu50Og9fKfSuxCeBW678/VPgA6aEe0IIQS4ckbHClxJBmEOSk5Ox2+1YrVY5\ncTdu3CjkeWmuAAAgAElEQVQjJ4RtNRKJ4PV6o7KjZrJTCVNNX1+fzMiLdd7IyMg004XVaqWzUzY0\n0UwXgbGxMdxuN62trfT29kaZcZRQ7iREK6ns7GyZragswXo9oR7X+vXr5fOL/7377rv84he/EIdo\noonb7aa3txeAnp6euNltqampspNKRUUFeXl5cls8V8futUYimlz5rJlPRNaf3++PqnSp1MgjkYjk\nSxGRdb1NBbFMGAJCkIu5n5eXR3V19TTNeS58AtDd3U1ubi49PT1RmaRax52amsqiRYsoKir6yOaN\nFiSiaSzM1Yia63Q6ewGcTmePw+HI1XKS0MCFw0OtTSRCJDKVdSXSWZctW8bixYtlZMbTTz8tBfvY\n2BgffPAB4XAYl8uF1+uN0shj3U8tMI1G4zQNUA11DQ0RLjQbuijvOTIyQltbG/X19dJcE+8cMf6U\nlBQZdieq9ymF2R9auxgYGJDCVitNmpqaZCp7OByOGWus0+koKCiQETo2m02ZQfrfGnOhCSAzFEVZ\nVAG1U05o4ElJSTJe/Q+FgoICkpKS5PhSU1Ojdgjifc2VJo2NjaxevRqfzxdVDiCRmVbIHtGAJjc3\nl6ysrKiCXx83XCtvmCZpIQitJKYWYSM0i9zcXIqKili2bBlr1qyhsrKS/Px8AHbs2CGZ1u/309bW\nxoULF6KcH3KwGk05yrHOETOeqLxPKBSivb1dxo1rQUpKCgUFBbLpb1JSUlQEw0wru1ZhH4u5EyU2\nJLjmjDcTtaKBaTXWlcjLy2PlypXAlHNNlGm4Fn6BjxiaGOyKyS5hWVOdTicXcovFMmNXnmuJWPQW\n+QbqY9S/Y0ATTZqbm4GpsF/1e0/E16KEsd1uJyMjg9TU1Blrfv8hoJWH5yrIex0OR57T6ex1OBz5\nQN+MZ4AsXPTuu+9quslsBKiyeWpaWhqPP/44jz/++LTjtBah0dJ8t7Ozky984QtynHfddRfHjh0j\nJyeH2dAFkMWNvvjFL/LFL34x4bFa4mJBOxNcy+PUEzQrKwuPx0N2drZmmnzyk5+UC7RWHhBO6o+D\n8J4LTQD+/u+ncmPEIjcTnn322TmPcb6Y7XuYK012794NwKVLlzTdZzYy5ePASwJaK+jorvwI7AMe\nv/L3Y8BeLRcRRee3b9+OTne1uL/S8ah0PprNZpKTk9m0aRNf/vKX+c1vfiNr9qqL1CtT/H0+H7/6\n1a947LHH2Lp1K0VFRTLIX6SUJ/qBqS1hTk6O1HLVjj2dTsfixYuniHPl86lTp6isrBSPq5kuMNVl\n6OjRo+zevVt2NIp1T7ia6arT6Vi9ejXPPfccr776Ku3t7VFmILUPQmkmUkbzKD/H+9F6nHrXdeut\nt/Laa6/NiibPPfccO3fulLQV4aLKDF+LxcIzzzzDuXPngKvVBNWlHuYLdThrrPDWmY6JR5Mr52vm\nk09/+tMAMpxQ0EbpvDcajbKs6osvvihb9cUb+0eFme4/Fz4B+Ku/+itgar4q6aGeN8qGDSLqbO3a\ntTz//PPU1dVNo5EY88cFWsIPXwI+AWQ5HI424JvA/wu84nA4ngRcwC4tNxMODmFHjgXlKpiWlkZO\nTg6rV69m69atVFVVkZqaOm3rpF45TSYTFRUV3HbbbaxcuRK32y2PeeyxxxKaBMRxjz76KB6Ph56e\nHtmFXXmeaIja0tLCkiVLZMnSgoIC0tLSAG7TShdA3mt4eJjR0dGEThsls4lu7UVFRTLMbCZNYq4h\neTMd9+Uvf5kTJ04wNDTE9u3beeaZZ/jc5z7Hs88+y6uvvgoaadLZ2Rkz61c8s8ViISUlhbS0NEHr\nj8S5GYtvZjJNfeUrX5lGk6eeeoq//uu/nhVN4GqEhjLLN9Z41N2Q/rtplko++cQnPsEXv/jFOfEJ\nIOufix20OgQzHoRZRcS4/3ej0WyhJWolXs+yHbO9WU9PDxBNdCXUNnObzUZRUREOh4MbbriBoqKi\nmKnI4lwBk8nEkiVLyMjIIBgMEgqF5Hmf+cxnYo5NLcgfeOABXC4XDQ0NBINBWltbozQ98SxqxhER\nCpFIZOdsaCP6Yw4PD8uJGk9IKMcqnL+FhYUxve6xtHLR7QambOzKhs3xEO845aL6D//wD7IutTLu\n+8c//rE4VhNNurq6Epq2RC1n0ZcVrqaof5RCXAv+5V/+BZDV6+T3giZ6vV4zn4gs5plMa0rtc6Y6\nRrOFVn9KouO+/e1vxzxutnwCyMiyeDX640G0iUxJSYkryK8HL82HfonG85Fmdh4+fJhdu3ZFRWQk\n2uaJHofClBIvekENnU4n6wfb7fao1G6x7YwFpVAW4UjJycnU1dWRkpJCOBzWbGOfLa4kQsiaCjM5\nbYSzJjU1lfT0dOx2e8JMzkgkIpOkurq6pCazY8cO2bE7EeIdJ8YpKhLm5OSwYcMG/H4/Vqt1To42\nn88Xd9cmojJSUlJISkr6yBxUiSbRtfZFxIMQ4DMJAw2OxD8aaF3c1BBlO5Q1geLt8K8V1O9ttgEI\n8xLkcTI7vwk8zVWHxPNOp/PNma515MgRABmRMRNDitR8pTCPV45V+Z0Q5LE0VOFEmwnCRp6amsqh\nQ4dkg10hyEV3mfvuu0+ek5mZSVpaGhMTEzgcjtNooItYPNSFxGaK6BEahd1uJz09HZvNNk1oKhem\nSGSqkevg4CCNjY2cO3eOSCTCjh075HtJhFjHKW2Q4XCYY8eO4fF4KC4u5vvf/z5JSUl8//vf55VX\nXiEzM5O6ujpNNFGGkqkhojJEpUp13LjWrXU8xDs/nm3XaDQmFCJ/93d/J7Nd9+zZA8D3vvc9fvOb\n38yKJhAd9ZUIsQS51uikjwJf//rXp2W7fve73+WVV14hKyuL2tpazTQRoZiJzE2xIBqDKAX5R4Xr\n8R60aOQ/Bl4Afqb6/l9nqD0+DR6PB4gfOaJ+QL/fT2dnp+yFKBJflHGpWsPiZoLyHHFNZQ9OdSsw\nr9fL0NCQLBkrMDg4yNDQEJFI5AYt9x0dHcViseDxeOjr65OMOdOKnJqaKk0qKSkpCTvdCI25q6uL\nhoYGTp48KWs1Axw6dEgTzQ4ePDjtO3Hf8fFxAoEAGzdupKOjQ5bfBGQEkV6v10STRIWV4KodWPm8\n13pyKJ3oExMT9Pf3097ePi1Ja/v27VFljNUoKytj2bJlvPLKK/I4l8vF1q1b2bFjB9u3b9dEE2Ba\nlrKWsf93Ed5K3H///ezevZuvfvWrUd8//vjjPPHEE+h0Os00mU12uBLqAIKPEmqlQynLYpmMtTzb\nXDM7ITqKRRNmEuQQrRGJ2ipCkIt6zkpBDtqF+Wy1EmW0hHrljpXZGW8siaAW5Eo7dCKzU1paGosW\nLZK28ZlaeAlBfv78eU6cOBElvA8ePDgvQS7svxMTE3LHo8zcne0kiyXIlbSIJcivFyYnJxkbG6On\np4eamhq6u7uj7r19+/aYdFFCbP/FcS6XS5rGtm/frnksYvcx07tWRhnNd4dyPbBu3TplBrTEXBYd\nYYLTeq5acP4haRNv5xTvu0SYj438GYfD8QhwCviylgI3ItZXVC+cCWLytre3c+DAASYmJmTih+g6\nozz2WkOUwxU/Wms32O12HA7HD9BAF6/Xi91ux+Px0N/fHzfZQ810IrU4Pz9/WqEf5Tki/E30eGxt\nbWVgYCDKBp2oIp3ynsp3Jr4XNLFYLFitVtLS0hgcHIwy9fzyl79k37591NfXa6KJyWRKSGu170SN\na2nrFLRzu92cP3+exsbGaWavw4cTdTecoq/NZpPHeb1e/H4/Fy9e5KWXXtJEE0CWV03kdxDObEA2\nV5mtwIrnYNfr9dTV1dHX1xflL1KH+t1111289VbsMuLC1yX8ZG+88QaTk5M0Nzdz8uRJXn75ZVpb\nWzXTZLZZzOIYwUPqUiHXEmplc3R0FLfbzcDAQFQQhjqAY9u2bTF3edu2bYt7r7kK8u8B/9vpdEYc\nDsc/AP8K/MVMJwlGFCUt40GtyYktrclkkoXyhclDHZUB0fZlraF26skfiUQYHx+XL1vE4yaC1+uV\n9v/KysoeNNDF6/VSXFyMx+OZ1gVIjXiCXKQWx9ql6HS6KEHucrmiyp8mgnr7qc4Y1el0UlgkJSWR\nmZlJRkYGg4ODpKamYjQaefjhh2Ws77JlyzTRxGw2x6W1eC+iichsamvMBZOTk7LRx4ULFzhz5sy0\nY2ZyFhsMBm655RZ5nDKCpbi4WBNN4GrS20zlicW1lbHR84W4xsWLF7lw4QJDQ0NR3ZqEQDIYDNx1\n111RzVaUEI1fhMKyZ88eJiYmsNvt/Pmf/znp6en8r//1vzTTRPC+OipoJgh/l1KQX2/tXJTibmho\nkAJdVGxVvqdt27bx/vvvTzs/kSCfFqQf66eysnJRZWXl+dn+T/3T0tIS+WNCR0dH5N577532PRDR\nShe/3/8Rjvj6Ix5NJicnNdOkvr7+Ixzx9Uc8muh0Os00ifyRzZ94NNm/f/+saPLHxivxsHPnzkgk\nAR20auRRmZ0OhyPf6XT2XPl4P3BRy0W+/e1v88ILL1BWVkZLS0uU5hyJYRNVapU63VT7o+zsbDZs\n2MDdd9/NDTfcQH5+PhkZGTM6x8T14jXKFfeEqdV9eHiY2tpazpw5w759++RWUTlWo9HInXfeKa+h\nMhlposvrr7/Ogw8+yPr166mpqUlIEyVd7r//fp566imqq6unOYDFM4hrjI+PEwwGaWxspKmpSW7r\ndDodf/mXf8n3v//9KHq0trZSU1NDc3MzfX19+P1+ubVW0khoQZOTk1RVVVFVVcXWrVuBq1qhx+NR\ntpzTRJPPfvazBINBnE5nzDBMUXvnC1/4Al/4whcoLS2N2VhhLhqWoK+418jICH6/n9/97ne88MIL\n1NTUxDw+FpTZlrfffrvUpAWfXNnhaJ4/3/jGN/jZz35GUVERXV1dMXlFr9djt9sZGhriO9/5Dk89\n9ZRsnKLGTLtT9Xd6vZ6GhgY8Hg+BQIBwOIzBYCAYDHLkyBFOnDgh+8wqE/fE+ZFIhPLycpYvXy67\nWL355pvSzFFWVsaiRYtmRZNdu3Zx7tw50tLSErZDE3Nf8HBWVhZ5eXncc889PProo1RVVUX5PuLt\n6GcDJR1FZNqePXvYv3+/bOEo5lEkcjUDeGhoaFpXp5kw18zO7Q6HYw0wyVRjic9rudmaNVOd4TIz\nM6elkyeCYAKfzyc7uicnJ9PX10dpaSkPPPAADQ0NpKWlkZSURFJSkqaC7LFe0vj4OGazWfb/O378\nOJ2dnVGV1SB2ZqcIibvyAm/RQhdR03ymQkhKphBhh3a7XfoM4j2X2PaazWZphhEx3wLr1q2LOt9g\nMNDQ0DCNPsoxKLUBmDL1DAwM8NJLLzEyMsKtt97KM888w4kTJ6irqxNCSxNNCgsLp5l/lGMZHx9n\nZGSE4eFh3G63FOSiaNa13CLrdFOlJGw2G/n5+SxatGja9a8IHylYR0dHGR0dJRQKyXfU0tJCaWkp\nAwMDJCcny16iaKQJIDs/KZuDqxGJRKT/Y2RkRNbQn+0zq68pvsvMzCQpKUmatvR6vUyYy8rKkkXq\nlHX04arj1WKx0Nvbi9PpJBwO861vfYuHHnqIS5cu0draOis+gavhxGqaxFIMlb/D4TBDQ0MMDw8z\nMjLC+Pj4da+eGYlEGB0dJRgM4vP5GB4ejlKIlGOeqUenGnPN7PzxrO5yBevXrwemGp2azWZGR0cl\nk8QrLysgVtRwOExrayuDg4OcO3eOJUuW8MADD3Dq1CkqKirIzs6WDZTNZvO06IZEL0qE0ZnNZul8\nOXz4cMySsrEyO5XEj0Qin9JCE+G9VwryRHSAqYpyaWlp2Gw2LBbLjFmNer0ei8VCTk4OaWlp064v\n6sOI73t6etDr9ZpKBQikpaWxc+dONmzYwC233CKvdf/99yu1ak00KS4ujqlBKhtsiC5C4j2IZs1i\nXNdqQooQ1PT0dMrKyqRWp1xcRSld8f3w8DDDw8P09PTQ29srdy2Cx5VdqrTyCUwX5LEQiURklmMo\nFCIcDl/TpKnU1FSsVmtUKepgMMjixYtpb2+X70M5p8XnSCRCZmYmu3btYuXKlaxfv55AICBpI0J8\nzWazZpqI2kixGookmkOie5jX65WC/Fq3RoyFWBFX4jvlXJst/36kmZ2icNXq1asZGhqSDRSUSPQA\nSs3c5/PJlnEABw4cwOVyybKZogO1xWKRwm7Dhg2cOXMm6kUriz2JdnJ33HEHhw4d4tKlS3R2dibs\nYD9fqMsWqBHLtJCWlkZqairJycmYzea4NFNvEwUt1BD1SsbGxmT9b9HcOpHTUXkf0ehX1JSeDyoq\nKhJ2tYlEpiIzent7uXjxIvfcc49sTBxrjPMR6iIDMD8/n/Xr11NSUjItCuQTn/iEPBamzEmiUbjb\n7Z511mE8CHOEyFqOx4tCIITDYUKhUJSQm8sipzxeNAUWc1H8z2q1YrVaZ+xGJLKRRWmFlJSUeb2n\nJVcqqqrb2c00T0Wyoc/nY2BggOHhYc1t1a4H5itXtJhWiplKBspjypTyotPp/Pe59O0UUSvbtm0j\nKSmJ/fv309fXJwVqvFUcYkdkhMNhKQjfeecdrFYrFouFpKQkaXoQ9TjMZjMbNmxg79698h6iefPY\n2Bijo6OEw2GGh4e544472LdvH263O2Z4ktFoJD8/H4PBwH333Ud6ejpDQ0Nz6tmprhWhZaIJbVyY\nkGazdVY/i9IWODo6ytDQEAMDAwwMDODz+RJmzAm7qSjQ/8tf/pJgMIjZbOYzn/kMjzzyCMPDw7Pu\nT6moIBlzzOK7jo4Ojh8/DkzRT0s8/WwhzFLFxcVYrVb5npTv6N5775XfRSIR2aZP+FmELba0tFQm\nkglecTgcTjTOn7KyMmBKaKpt4+pFG6YW5lAoFFXieb5Qzkclr4o5rPy/uuqj+HtgYIDnn39e+mkE\nr3i9Xv7mb/6GDz/8UDNNKioqJE2U44sHpV9hcnKSQCBAb28vbrdbln241uY5JYTCqKaT2sw822gs\nLRr5OPA3TqfzrMPhsAE1DofjLeAJpvp2/n8Oh+OrTPXYS9iaSazWy5YtQ6/Xy7hpEXo3Pj4+TXtR\nMqZ6OzIxMSGTLUQ/Q2HTTElJkbZkZd/Cw4cPRwlyYS8Wglxcz+l0xs2Mi0QiuN1uwuEwly5dorq6\nmkAgQFpaGsFgkMHBQSorK0XfwRnbmkF0klQ8YStgs9lkP9L5CC71fQKBAN3d3fT09ODz+WasK6PT\n6UhJScFms5Gdnc2jjz7KTTfdJE0qN998M6+99hqbN2/m6aefxuFwaKLJkiVLZGKU1WqVYWLqe3s8\nHurq6gCk8y81NVXaysVxs4FSICp/K7VINSorK6OcpKIGTEZGBjqdDrfbzRtvvIHRaKSkpCSKVzo6\nOhxa54+wB2dlZWG32wmFQgnbGAYCATwejxRyAvMRVPF2ArEUrkTO+ieeeIJ77rkHv9/PAw88wJYt\nW9izZw+bN2/mxz/+sWaaLF68GJjK3xBhq1p3QJFIhOHhYVpbWykqKpKmx+uFWDQSn6+7Rn4lOqXn\nyt9+h8NRBxQzx76dANnZ2dLmXVJSwrFjxzh9+jRerzfKfiig3MqqzQzKSm/i+4mJCUKhEGNjY/j9\nfkwmk/x/bW1tTC1CxEMLDTRRerOSWYSgMZlM2Gw22tvbxWGaaCLizuMJzVgTTphU1LZaLZMz0THD\nw8M0NzfT3t4uBWms45VafFZWlmx4vXr1arn1Li8vp6enh3fffZef//zn4lRNNFE6O3NycqISmJTj\nUS42Fy9elFER6m32tUIsflBqxrGiNJT1eYSza668IoRMQUEBeXl59Pb2Mjo6GvedDg4O0t7eTnp6\n+jXVMuPxRLz/qY9LTU2N2l2Ul5fT29vLe++9N2teKSkpAaYWN5vNltAcGAtDQ0M4nU7y8/NZunSp\npmeYK66nI3VWNnKHw7EYWAMcA/Lm0rcTkBpyUlISOTk5UiB1dXXR09Mj642YzeYoTWwmR6WYaGLy\nxBKOwhQzE5Qp+ULIx0JHRwcWi4VQKBQVfqiVJsJBOlPRH+Xzmc3mqMVJfVy88yG2iUoIncHBQZqa\nmmhvb5fvQH095aKq1+vJy8tjxYoVsjej0Wiko6ODuro6Vq9eTX9/vww/1EqT9PR0eU5ZWRnj4+Mx\nvfiimiPAqVOnMJvNZGVlTauNozbVXc8JlQhGo3FevCIWqNLSUhYtWkQwGJSKT6xnGhoaoq2tTdrW\nrwXUioPWQIJY1wHmzSvCJ1NYWEhBQQHd3d2SJxL5EQSE8lJSUkJ/fz/FxcWYTCbp+LwWfpaPApoF\n+RWzym+AZ69o5moKzWpvYDAYZC3t7du3s3TpUlpbW2ltbaWpqQmYmtDC5ALxt2qgvcSnlnEBsgSr\nTqcjFAoRCoVimjy+9KUvSTt/DMw4GMF0s9EiZmsXnwliJ+LxeLh8+TJtbW1SI4/1zGKCGAwGioqK\nWL9+PcXFxRiNRgKBAF/60pf4+te/Lm25KsxIE71eL7XP9evX4/V6ZXszpQasHNsHH3yA2WymqKgI\nk8lEWlpalL9FnDsfzLRIqr9Tfh8IBMjPz58Xr4h3XllZSV9fHz09PQnbvg0ODuJyuWRUzUzPMV/M\ndu4FAgGeffZZnn/++TnzinBwL126VNaxHxgYiBkkIP4Wi7lOpyMQCNDe3i7jugsKCqR2f62F+LUO\nlJh28Zl+KisrjZWVlW9WVlY+q/iurrKyMu/K3/mVlZV1Gq71R4OxsbHIk08+GfnJT34iv7vzzjsj\nbrdbZHZqosmFCxf+AKO/PkhEk0gkopkmH/W4rzfi0cVgMEQiC/NHfqfglT9JmiTClKief2bnj4Ba\np9P5b4rvRN/Of2aW/SkjMVbIiYkJGX/rdru56aab+NnPfsbAwIAsDTs4OMjg4CB+v19qyYFAgMuX\nL5Obmyudpcq+jWLlNRqNDA0NUVRUJBOKUlJSsNvtpKWlkZGRIWPQv/a1r/GTn/wEk8lEIBDgrbfe\n4oMPPpAZkTDleJqYmOCHP/yhXLGzs7Nn3bNzw4YNcqsdy3yjtIOLZ3rwwQfZuXMn9957L7m5uXG3\nuolor6RNW1sbly9f5p133mHv3r20trbKGHJxvDLhxmg0kpOTQ2FhIQ899BAPPvggdrudb33rW5SX\nl0e10xO9GD/3uc9ppglM+QzMZjPHjh3j1Vdf5e2336a7uzuqbZ+SkQ0GA4sXL8bhcLBx40a2bt1K\nRUUFmZmZsi69WkMXNFBjphBY9bFiNyXeU3t7Ox0dHfziF7/g5z//OampqXzpS1/iySeflA3Bs7Oz\nlc7TWc2fnp4ezp8/zy9/+UveffddvF6vrHsixjg5OUlmZibZ2dn8xV/8BU899ZRMWJurOURJAyXP\nBQIBDh06xKFDhzh48KAMKIjlM7jvvvvYvXs37777LhUVFTz66KOS7+fKKwCNjY2cP3+el156ibff\nfnuaaVXcX/3s4jkKCwuprq5m8+bN3HHHHWzcuJGxsbEZk+1mopPy2M7OTn7961/z+uuv09zcjMvl\nitrhqufmbKAl/HAL8OfABYfDcYap7c7zTAnwl2fbt1M8lJoZTCYTWVlZZGVlydjQT3/60/j9ftxu\nN319fTKsS4TGDQ8PS6dYWVmZzDZTCnQRHie2YOXl5dhsNlJTU0lLSyMrK4vc3FwKCgooLS2VSRcP\nPvggRqORgYEBOjs7OXnyJOPj4zIu1263Ew6H+dSnPkVpaamMvCksLJxVz04R1z2boj/Kyn9qwaKk\nqfp/6mOEcO7t7eXChQs0Njbi8XhkWFi8axoMBnJzc6mqqmLRokXk5eVx9uxZXn/9dSorK/nUpz6F\nTqfjueee4+mnn551f8pIJCLfV2lpKVVVVTKeP1ZylngWl8tFZ2cnwWAQu90uF56srCyZph6LXrEm\nTTzaJRqz+jqRSET2hjx27BglJSVEIhHJK4rwQ03zR9wjJyeH0tJSlixZQmlpKS0tLfj9fiBauIhk\nl97eXkk3wW9zERZaxqcFLpeLgwcP0tPTIxtKP/fcczz11FM899xzfPvb3541TUpKShgZGWHRokXk\n5ubS399POBxO6IRVjre/v5/Tp0+j0+koLy9n48aNMnRTyf+x7j1bXrkepi0tUStHgHhR8rPu26nE\nTA8kJoGwp+fl5VFRUSHDrkZGRqSN+amnnopqCadcgUVIIsCTTz4pY81FE18RQiey1gAZ2ieuoxSa\nIyMjNDY2AshVFaZs1x6PB71eTyAQ0NR3MCMjQz5rLIdnLGYRCQyjo6PTMhqVtI3HYGK8gUAAu91O\nfX29TIASdVWU91f/bbFYqKqq4tZbb6WsrAy9Xs+6deuora2ddm+dTsdPfvITcYlZ9TGFKcf4qlWr\ngKm4aNF8IxgMTtuBiJ1da2srv/vd72hubmb58uVUVlayZMkSysrKmJiYiKqaqVX7nsnRHu/70dFR\nLl++TENDw7RdV2dnJ5FIxKGNEtG7qPT0dNatWyfT8JWJdcrxTExM0N3dzfnz56mqqiItLe26JL2I\nUF4tysiiRYvYs2cPVVVV0xS6K7wya5oYjUYyMzPZsGEDIyMjHDt2LGalykTjDwQCtLa28vbbb7N7\n924aGhpYtmxZTOengFZBfr2dpR9pZqeA1hXMbDZjNpuxWq3KwktREIzz6KOPTtueCCgn/O7duzU1\nJRAtvJTa/UzjNhgMs+4jKbzuZrNZRoqoob7vwMAAXq+XYDAo613MRqMXdPL5fNjtdmpra/nwww9l\nH0/1PZVbdqPRiN1uZ9myZdxyyy0yVlqNa8W4VquV6upqMjIyaG9vp76+PqqTklLjEoK8ra0Nl8uF\n0+nE5XJJbbSsrIxgMCgzI8VPLMdxop1OrGOVJkLlrvB6QKebKiC3Zs0aWcjq4sWLUfXHBcbHx+nu\n7ubs2bOkpqZSUVERFc2jXnTnAvHc6lLPc9FW5wqDwUBGRgY33HADFouFvr4+Ll68KBW7RBDmsfHx\ncSgEl1sAABXbSURBVNra2iRv1dXVUVhYiM1mi+eMTYhYz67F/DkX/MEEuXj58Zg9OTl5Wtd2g8Ew\nLQlGqQ3H0yTnYhMMh8P09fXR2NhIV1eXLNiVCDabjaKiolll0jkcDnnu0NDQNJtZrOfo6+uTgspo\nNMp2b7FCBWMxkyhxcPLkST796U9z4sQJuTVXn69+vqqqKlavXs2KFStIT0+P29TiWkFoW+np6Wzc\nuBGj0cixY8c4fvy49JPEGitMhZY1NDQQDAZpbm7mtttu49e//jWFhYXk5eWRk5MT1UlddFOfabIp\nd2kmkwmv10soFGJ4eBiv10tTUxNNTU20trZG8XcsbW62tBAwGo2y3d+WLVsYHx+ntraW5ubmaeeJ\nDNjCwkJWr14tM53nah9X/i14LBgMMjQ0FBX6J35fT2GuVjKysrIoLy/npptuIhQK0djYKJuBqI9X\nP5NOp4sKdX3zzTfp7e1lxYoVOBwO0tPTZVXCWM+lhZ7qOX2tMJcU/f90Op0vzLUBM1xdwYWtN5b2\nk5ycLJlC/F9UJ1NONPFbazieVuYdHR2ls7OT+vp6KcgF4qXoi8bLIyMjmpsvV1VVAVebBigFuYBa\nYxKCvL6+nrS0NDIzM2MK8ljPG4lE8Pv9dHd3c/z4cSnIlbuBeDSyWq2sXbuWnTt3Si1ZoKenh69+\n9av09/ej1+vZtWsXjzzyCN/97nd5+eWXZ9VUV60lGo1GMjIy2LRpE+Xl5UxMTNDU1ITH44kZ7y4E\njHCeNzY2YjAY+NGPfsSvfvUrVqxYwcqVK1m2bBnZ2dmyDLDSBBdrLMrri8JdQpAPDg7S2dlJR0cH\ntbW11NXV0draSiQSoaioaFqKflZWFqmpqYJP0EIX5f0NBkOUILdYLAQCARm6K46DKUHe19fHypUr\nGRoakjtdNb20Qr3znZycJBQKRQlyQTv1WGDKdv+3f/u30heza9cudu/eLRsw9/b2aqaJenHLzMzE\nYrGwceNGTCYT4+Pj0gyaaEwC4+PjMjb/zTffpLa2lrvvvlvyR1pa2jTZE2/RikfTP4ggJ3aK/ttX\n/jerBsxKG18gEJAONq/XK5vaChPBN77xDV544YWoc/Py8li8eDHFxcXk5+fLLjRi8mmxd8YSbCKO\nWhTRKS4u5p133uHcuXNcuHBBpv8rz3e73YyOjsoU/bGxMdHijdtvv52nn35aUwPZlStXAkhhorQz\nJtJmRPs7t9tNc3MzxcXFZGRkyGSriooKmpubZclXpcbocrloa2vjwoULwNWs0nj0E9EVt99+Ozfe\neCMOh4PMzMyoYwwGA1/72tdYvnw5gUCA+++/n82bNwPwxBNP8MQTTwBobqobCxaLhczMTKmZX7hw\ngYsXp8pW6/X6mOUdBARNXS4XIyMjdHV1cebMGWw2GzabLaos8O7du9m7d++0VoJi+y0SkUQc9A9+\n8AP8fr/MTO7t7aWvr0804ZYp+gaDgdLSUlkG4kpdG800iSUwLBYLhYWFrFq1iqGhISwWS5Tggqut\n/Orr63njjTdYt24da9eulU27xfNpub9yHgkB7vF4ZOTT4OCgvF48k4Zer+fJJ5/k7rvvlin6Kl6Z\nNZ+Isel0OlkbJxKJEAqFMJvNMRe4mRavUChEb28vR48epb+/n6KiIgoLC+Xib7fbWbVqFd3d3dMy\nrcVCPzo6SiAQYPny5Rw8eJBLly7R3d0dcwesfh71eBNhrin6ReJ+M94hziD9fj81NTW89dZbuFwu\nurq6ZClLIci/853vRD1IVVUVmzZtYsOGDRiNxrha1GwgJqgQ4h0dHRQXF/PWW29x+vRp6urqogol\niRc1Pj6OTqfDarUyNjaGxWKRphXRXEELhCMvJycHi8UiFxXBFOpCRAKiZOjly5epr69n6dKllJeX\nU1BQgM1mo6KigqamJkZGRhgcHGRgYID29nba2tpobm6mqalJPpdwsqqZR9xPCPKdO3eyZs0aKisr\npSNYnJOTkyP9GCLtWhQEm68GopygJpOJjRs3smLFCvbv3y+vrdfrGRsbi9qxKd+XECptbW10dHRE\nNdUWmlxWVhZpaWns3r2b1157Lao+tRDioVAIv9/P8PAwg4ODPPvss/yf//N/CIfD8j5Kn4rSZi2c\nn6J64HwgnstkMlFQUCCVmszMTPbt2xdFO2HCrKurk03Dly5disVikeYkLVt+tYkyEokQCARwu924\nXC4aGxujEvOUipsSdrs9Zoq+OG++NDEajRQXF8ty2Tk5Ofz+97+fcVzqngPhcBi3283g4CCnT58m\nOzub3NxcHA4HDoeDoqIiVq1aRWdnJzabDZPJJDN2RcEyn8+Hx+Nh+fLlHDhwgIsXL9LV1TXN2qCm\nsXqBnYkuc03RPw7czBwaMAskJydTXl7OmjVrCIVCdHR0EA6Ho6oNquo209bWhtlsZnBwkIaGBvLy\n8rDb7ezevZs333wTs9kssx6FIFTaxLZs2cKhQ4eimioLzcrv9+Pz+XC73WzcuJGzZ8/S2dlJKBSa\nplmI8Yl0dKvVyoYNG0hOTubkyZPCLqepgawwT6xfv55QKERdXR0ulyuuo0T8npyclAXHhBmhvb1d\nNte488472bt3L2NjYwSDQfx+PwMDA/T39+PxeKKiU5QCWUwEi8VCdnY2ixYtYtmyZQCsWLGC3Nzc\nmIJIOSk6Ojqor69n1apV1NTU8Itf/IK9e/dqbr6svp5S29Lppgp1mUwmVq5cKRn+zjvvxOVy0d7e\nTiAQkDsbtU1UXVNHXF/U5RFp8BcuXIiqTy0EtCj1GwqFpFnH6/VG8Yg6EkO52FgsFiYmJmQug8Ph\nOMss54+SHjDFh8nJyRQXFwNXs4VvvPFGWltb8fv9BAIBhoaGaGlp4dixY5hMJpYvX84tt9xCMBiM\nqms/EyYmJggGg/T393P27FlqampwOp1RpYQzMjIwmUwysiyeP0yk6K9atYrTp0/zi1/8gn/+53+e\nM03E3yLwoKioCIPBIHedt9xyC62trbIBtlDI4gnKSCQS1dtT1Hh3u91kZGTwyCOP8Morr0xbFJW9\nSX0+Hw888ADnzp2ju7ubcDic0HcCTHNaz7R7mE+K/pwaMAvYbDbWrl0r21I1NTXJbh0CantTf38/\nPp+Puro6UlJSSE1NJSsri927d/PKK69IJ45YGcVEFIJvy5YtvPnmm7J7i2Du/v5+hoaG8Pv9BINB\n/uVf/oWLFy/GLGGrnKRms5kvfelLlJWV8Wd/9meUlpayfPlyUlNTWbFihaYGssI2vnnzZoxGIz6f\nL6Ygj+UXAGRyVFdXV1SNke9973u8/PLLUisUwks5odRbdaUvwm63U1FRwY4dO9iwYQMw5ZgVoXvx\nGEuk6Iu0a9F8WafT4XA4NDfVVUN5P/GMK1askJrdZz7zGY4cOUI4HKarq2uaIBdQdy0X9AkEAgSD\nQfk/0WZOQG0TVmr5kUgkqrmHMlJCnBcIBCgoKKCvrw+z2YzFYsFkMlFXV7dmLvNHSRex+Obl5ZGe\nnk5WVhYAW7dulaWeA4EAPp+PYDDI0aNH6ejoYMeOHVKQizmj5X4TExN4vV6am5t59913efvtt/F4\nPMDVOPXs7GxsNhv9/f309/fLnYG4hqCJMkX/4Ycf5plnnkGn082ZJmqBnpubK3dbMLXgv/feezQ3\nN8vCeqAtwkQkBA4ODnL58mVJr5/+9KfTjlUGK4jEwQsXLsjIHvUcUmveiXYOMaG8YbyfWCn6qv9r\napY6OTkZ+WNBrBRjgdk0Gv5ToUkkEplNU90/KsSjy9e+9rVIZBbz5yMd9HXGDLzyJ0mTRLj//vsj\nkQR0mHOK/lwbMCsXD+EQ8Hg89Pb2cv78ec6fP09dXR379+8nPT0dn88X02kiHKMGg4FwOCwTh+Jp\nrjCVvZWZmRmlpSoTGdSJRALiPnl5eeTl5VFVVYXH4yEvL4/HHnsMl8tFZmYmfr+f3NzcWTfVhSnH\nighdq6ur49ChQ5w4cQK32y3joIW2Fy86Rfnc4+PjMiQzksC+FolEsFqt0q6+cuVKqqurqa6upqSk\nhPT0dKxW67TmzrHo+/zzz1NRUcFjjz0mv3O73bNuvpxorLEgfC4iSqWjo4PW1lYaGhqora2lpaWF\n3t5ePB5P3FII6vuoHZ3x7q/mFaVtXnSrslqt7Nq1i/b2dl5++WVZV/zJJ58Up82ZLsox/f/tnU1s\nU9kVx3/5xHY+/IHt2CRpnARxwSBEB3VQBAoDog2qKg2rCGbTItENGYmy66y6bReV2qrtZjqqRlWr\ntixaBqgqtcqiqhDqBAmpRNVNQSohJHUCBHAQiUaQLp7PzYvz7NixCTG9fwmROMfnvft/9553P87H\nqoHd2EgmkzEJxx48eMD4+Djj4+Pcv3+f6elpGhoamJmZYWBggB07dtDV1UVXV5fJdy6RsHJ+dO7c\nOS5cuMDMzIwpyh2LxUgkEqTTaXMIfuTIESYmJmhqajLbeZcvX+bSpUscPnyY06dPMzo6uiZE/+HD\nh8TjJuFh2ZwUej7gbFVIpHY2myWTyTAzM4PWmtu3b3Pnzh3u3btnUibLOVUh3fLMJXq82D3I58Vm\n1uJJFI/HuXv3LmfOnKG3t5fu7m46Ozs5f/580bZXEqL/QbkFmPM7vCyRE4kEoVDIuFRJRz9x4gRP\nnjzh+fPnvHjxwkSxyZ6VOwBBikOIkQZvX/KFhQUz0MRAy/5WY2Oj2ePbvXs3fr/flIsLBAJ0dHQQ\nj8dpbm5mbGyM5eVlTp1yygtevHiRa9eulV1oWODz+aivr1+VTzsYDJq6j7KPD06eFymE4T5Yyzfa\nso0g7ZTOIiXfJIr12LFjhMNhent72bVrF/39/fT19REOhz0zLXp1yJs3b3LlypU1IfpXr17dMCf5\nKLaX2dLSgt/vJxqNmu2FaDRqooElffGhQ4fMEnlxcZGlpaVV/SnfY0j6idtAy7aduPD19PQYXn0+\nn4kWjkQi1NXVMTY2xo0bN1hYWOD69euMjIwwOjrKxMQEExMTtyijgLkXJ7DaUMjzisVixs2xo6OD\nYDBIPB43hl1cauXAXnLjP336FL/fb85CxKUWHA+nQCBAZ2cnjY2NJqXuzp076evrM1uF/f391NfX\nE4vFyGazxotHKUU2m/UM0Zfxs1FOvDzS5DkKJ+ILLlyEQiEikQg9PT1MTU2Z8yOAdDpt9sTF7kgO\nIvf2Wf6kT/qMjDuZFCSTSWNj3P1FxmIgEDABgkePHjXeebFYbN0c+5WE6Jfk87oeZGD4/X76+vqI\nx+Ps3bsXcMLu5+fnTcrOR48eMT8/b9wVZV8TnJNweZPKHpR75iUPUsqjieH2+XyGRKkqBA6REjQS\nDodXdYCWlhZGRkbw+/20tbXx8uVL6urqGBwcdLer5AKysDILlCAbidybnZ1ldnaWyclJJicnASeN\nqSQSk5ebtN3dZncb/X4/wWCQUChEOBwmEomYGqpnz541nVrKyAUCAbPCWW82AXDw4EFTrccNNyeU\nWHx5I5B7dHPY0dFBOp029V3ByaEjL8e5uTlT91UOu92umG6dDQ0NpmhGU1MTgUDABIccOHDA5O0R\nzwY5iG9rayMQCLBnzx7m5uZMUZCTJ0/KoD9QTR7yn1d9fT2hUMi8WNLpNM+ePSObzRpXwaGhIeOT\nv7i4uMY3X9oOjoGORqPEYjEikYhpozuwSq4LmBz1AwMDdHV10d7eTjweZ3h4mPb29lUv5sHBQblm\n1TnJRyAQoLm5mdbWVlKpFNls1sQDiLfV8ePHyWQyJmGfuO8uLS2tObCUNss5lbzot23bZpK2KaXM\n6lfGobgySu4neREODQ2tqjm83vnFG4nszJ9Z1dU5QR9SVFiWV/v37+fx48dMTU2Z2XFzc7Mhy/2A\n8hMi5S+fhQgxbkK03+83JeGkvic4lUdkWSNki89x/gFEKTPWYly49ciDa21tJZlMEovFjKeIdJxw\nOGz8md3ZHr38qGUQ+nw+04HE0MhB4b59+0gkEqZ8XL6nx0ba9brgdUCU/3fhMBgM8urVK5aWlsws\nSyll2inyElIv3hX513HPwpuamswKTXKmx+Nxtm/fTiKRIJlMmi0KCe2WaxVKM1ENFHtm7jq2gDFE\nsmWXSqXMzFtWK+J7LgnnZEYYCoXo7u4mlUqZ+rRe1efdHjUSfRwOh42xKyeNxUZRrK+4X8jRaNR4\nmMzPzzM9PQ04Cdvk2YuzgPSRYqs39+revfKNRCLGYIu7bjKZJJlMmm04iZROpVJr2lC0rcX2Ty0s\nLCwstj6qW3LcwsLCwmLTYQ25hYWFRY3DGnILCwuLGoc15BYWFhY1DmvILSwsLGoc1pBbWFhY1Dg2\nzY9cKXUS+BHOy+MTrfUPisj+B3iKEzX6hdb63dznnwDfADJa6/25z8LA74AenGiwYa310wKya4ph\n4IQBuwtnfKy1/omH3u8AP6OKBTbK4aUQJ+XwgpOEyHLyFnNSjJc3yMkw0EaVC9RYm7KCTZmRK6Xq\ngZ8CQ8Be4IxSaneRr7wC3tNaf9k9OIFf5nS48V3gr1prBYwCHxWRBacYxju5f39mpXDGXmAAGMnd\nW77e83lyH7rakK+zJJTJSyFOCrXVixfLydvPCVQ+fqrNyUceshXxYm3KamzW1sq7wL+11ve01l8A\nvwXeLyJfh8e9aa3/Dsznffw+8Gnu50/JhYEXkBXdbp3/1Vrfyv28APwL6PLQ+1UPuYoKbFAeL56c\n5O6nJF4sJ/8XnMh1Njx+XgMnpwrIVsKLtSkubJYh7wTuu36fYuWGvbAM/EUp9blS6tvr6I5rrTNg\nqhnF15H/UCl1Syn1C6VU0P0HV+GMG0BHIb15BTaK6lwH5fBSDidQHi+Wk7WoVU7g9Y2fijnJk62E\nl63CCWyBvrJVDzsPa63fAb6Osyw5UsZ3i+Uc+DnQp7U+gFO+ztQbzS+c4aFnuYBcQZ1VRiWcQGFe\nLCdrUcucwOsZPxVzUkC2FvrKlrcpm2XIHwBfcv3elfvME1rrmdz/c8AfcJZRhZBRSnWAkyOdlQMC\nL71zWmsh82PgK7nvNeIQ+Sut9eVCer3kCuksESXzUiYnnvdfQK/lZK3emuUkd62qj59KOSkkWwEv\nb5wT0bcV+spmGfLPgZ1KqR6lVDNwGvjMS1ApFci9oVBKtQBfY3WC+TpW7x99Bnwr9/M3gcuFZHPk\nCdyJ69cUziig17PARgGdpaAkXkrgBErnxXKy+v7XyNUqJ7nrVGv8VJsTT9kKeLE2xX1Tm5X9UDmu\nQj9mxVXo+wXkenHemMs47pG/Flml1G+A94DtQAb4HvBH4BLQDdzDcRV6UkD2GM4+lLsYxk7gb8A/\nc9eUwhn/AH7v0vtD4E8ech/k65R9sGrxUoyTcnjBWbJZTt5STtbj5Q1yMozjWeIlu2FerE1ZgU1j\na2FhYVHj2KqHnRYWFhYWJcIacgsLC4sahzXkFhYWFjUOa8gtLCwsahzWkFtYWFjUOKwht7CwsKhx\nWENuYWFhUeOwhtzCwsKixvE/fiaUhbfKc/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157c9a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uv/9+rF27Ft/4xjcoL8WlUEGX2tpa5ObmoqSkBEuWLGEvkRi5xWJhR9rly5ez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\nIossznJkGXkWWWSRxVmO/wdOrNc61xNz0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e1c250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BMNGDJf5wHtuoQHQSr9fLwMAAZ8+eVQ5V9vl8MzbGstlsOBwOqqurWblyJZWV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\nTtSIvTd1dMHU1BRTU1Pk5+c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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abc5150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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09PDWW2/xu9/9jkceeUTuc25ujlAoxC233MJvfvMbcRpXBC/Rd4WA/Cvqjv/z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\nIQpHhVwIIQpHhVwIIQpHhVwIIQrnCzE2nNztm+vHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae367d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib_pyplot_samples(train_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cYznx5jUwzoO"
   },
   "source": [
    "---\n",
    "Problem 3\n",
    "---------\n",
    "Another check: we expect the data to be balanced across classes. Verify that.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notMNIST_large/A.pickle 52909\n",
      "notMNIST_large/B.pickle 52911\n",
      "notMNIST_large/C.pickle 52912\n",
      "notMNIST_large/D.pickle 52911\n",
      "notMNIST_large/E.pickle 52912\n",
      "notMNIST_large/F.pickle 52912\n",
      "notMNIST_large/G.pickle 52912\n",
      "notMNIST_large/H.pickle 52912\n",
      "notMNIST_large/I.pickle 52912\n",
      "notMNIST_large/J.pickle 52911\n",
      "notMNIST_small/A.pickle 1872\n",
      "notMNIST_small/B.pickle 1873\n",
      "notMNIST_small/C.pickle 1873\n",
      "notMNIST_small/D.pickle 1873\n",
      "notMNIST_small/E.pickle 1873\n",
      "notMNIST_small/F.pickle 1872\n",
      "notMNIST_small/G.pickle 1872\n",
      "notMNIST_small/H.pickle 1872\n",
      "notMNIST_small/I.pickle 1872\n",
      "notMNIST_small/J.pickle 1872\n"
     ]
    }
   ],
   "source": [
    "def balance_check(pkls):    \n",
    "    for pkl in pkls:\n",
    "        with open(pkl, 'r') as f:\n",
    "            dataset = pickle.load(f)\n",
    "        print (pkl, len(dataset))\n",
    "\n",
    "balance_check(train_datasets)\n",
    "balance_check(test_datasets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LA7M7K22ynCt"
   },
   "source": [
    "Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.\n",
    "\n",
    "Also create a validation dataset for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 411281,
     "status": "ok",
     "timestamp": 1444485897869,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "s3mWgZLpyuzq",
    "outputId": "8af66da6-902d-4719-bedc-7c9fb7ae7948"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: (200000, 28, 28) (200000,)\n",
      "Validation: (10000, 28, 28) (10000,)\n",
      "Testing: (10000, 28, 28) (10000,)\n"
     ]
    }
   ],
   "source": [
    "def make_arrays(nb_rows, img_size):\n",
    "    if nb_rows:\n",
    "        dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)\n",
    "        labels = np.ndarray(nb_rows, dtype=np.int32)\n",
    "    else:\n",
    "        dataset, labels = None, None\n",
    "    return dataset, labels\n",
    "\n",
    "def merge_datasets(pickle_files, train_size, valid_size=0):\n",
    "    num_classes = len(pickle_files)\n",
    "    valid_dataset, valid_labels = make_arrays(valid_size, image_size)\n",
    "    train_dataset, train_labels = make_arrays(train_size, image_size)\n",
    "    vsize_per_class = valid_size // num_classes\n",
    "    tsize_per_class = train_size // num_classes\n",
    "    \n",
    "    start_v, start_t = 0, 0\n",
    "    end_v, end_t = vsize_per_class, tsize_per_class\n",
    "    end_l = vsize_per_class+tsize_per_class\n",
    "    for label, pickle_file in enumerate(pickle_files):       \n",
    "        try:\n",
    "            with open(pickle_file, 'rb') as f:\n",
    "                letter_set = pickle.load(f)\n",
    "                # let's shuffle the letters to have random validation and training set\n",
    "                np.random.shuffle(letter_set)\n",
    "            if valid_dataset is not None:\n",
    "                valid_letter = letter_set[:vsize_per_class, :, :]\n",
    "                valid_dataset[start_v:end_v, :, :] = valid_letter\n",
    "                valid_labels[start_v:end_v] = label\n",
    "                start_v += vsize_per_class\n",
    "                end_v += vsize_per_class\n",
    "                    \n",
    "            train_letter = letter_set[vsize_per_class:end_l, :, :]\n",
    "            train_dataset[start_t:end_t, :, :] = train_letter\n",
    "            train_labels[start_t:end_t] = label\n",
    "            start_t += tsize_per_class\n",
    "            end_t += tsize_per_class\n",
    "        except Exception as e:\n",
    "            print('Unable to process data from', pickle_file, ':', e)\n",
    "            raise\n",
    "    \n",
    "    return valid_dataset, valid_labels, train_dataset, train_labels\n",
    "            \n",
    "            \n",
    "train_size = 200000\n",
    "valid_size = 10000\n",
    "test_size = 10000\n",
    "\n",
    "valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(\n",
    "  train_datasets, train_size, valid_size)\n",
    "_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)\n",
    "\n",
    "print('Training:', train_dataset.shape, train_labels.shape)\n",
    "print('Validation:', valid_dataset.shape, valid_labels.shape)\n",
    "print('Testing:', test_dataset.shape, test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GPTCnjIcyuKN"
   },
   "source": [
    "Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "6WZ2l2tN2zOL"
   },
   "outputs": [],
   "source": [
    "def randomize(dataset, labels):\n",
    "    permutation = np.random.permutation(labels.shape[0])\n",
    "    shuffled_dataset = dataset[permutation,:,:]\n",
    "    shuffled_labels = labels[permutation]\n",
    "    return shuffled_dataset, shuffled_labels\n",
    "train_dataset, train_labels = randomize(train_dataset, train_labels)\n",
    "test_dataset, test_labels = randomize(test_dataset, test_labels)\n",
    "valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "puDUTe6t6USl"
   },
   "source": [
    "---\n",
    "Problem 4\n",
    "---------\n",
    "Convince yourself that the data is still good after shuffling!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be sure that the data are still fine after the merger and the randomization, I will select one item and display the image alongside the label. Note: 0 = A, 1 = B, 2 = C, 3 = D, 4 = E, 5 = F, 6 = G, 7 = H, 8 = I, 9 = J. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pretty_labels = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J'}\n",
    "\n",
    "def disp_sample_dataset(dataset, labels):\n",
    "    items = random.sample(range(len(labels)), 8)\n",
    "    for i, item in enumerate(items):\n",
    "        plt.subplot(2, 4, i+1)\n",
    "        plt.axis('off')\n",
    "        plt.title(pretty_labels[labels[item]])\n",
    "        plt.imshow(dataset[item])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W7/dFUo2anDGO9i0nmPDfNpsNPp8Pfr8fDodDTGbKNbKzVKf7IUOTkpesE8/yOQm+j0Ag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\nBQoULCgUAlegQIGCBcX/AYRo8uRnWWu0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17c8998d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(train_dataset, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jJvBJmNUaf4yD+Dormpbpx8fHTEzTuAbopReLMXk8HqytrS31jjvKCa7b7aJc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c502d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(valid_dataset, valid_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zCHWDpvKxlH4rOs8/PGi1WozHY0iSxEwlclCUSqvVYlm9AmejUCiwshY0Z/MK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\nhMSS4v8BOTDJzk8lGp0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a123690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp_sample_dataset(test_dataset, test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tIQJaJuwg5Hw"
   },
   "source": [
    "Finally, let's save the data for later reuse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "QiR_rETzem6C"
   },
   "outputs": [],
   "source": [
    "pickle_file = 'notMNIST.pickle'\n",
    "\n",
    "try:\n",
    "    f = open(pickle_file, 'wb')\n",
    "    save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    }\n",
    "    pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()\n",
    "except Exception as e:\n",
    "    print('Unable to save data to', pickle_file, ':', e)\n",
    "    raise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "output_extras": [
      {
       "item_id": 1
      }
     ]
    },
    "colab_type": "code",
    "collapsed": false,
    "executionInfo": {
     "elapsed": 413065,
     "status": "ok",
     "timestamp": 1444485899688,
     "user": {
      "color": "#1FA15D",
      "displayName": "Vincent Vanhoucke",
      "isAnonymous": false,
      "isMe": true,
      "permissionId": "05076109866853157986",
      "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
      "sessionId": "2a0a5e044bb03b66",
      "userId": "102167687554210253930"
     },
     "user_tz": 420
    },
    "id": "hQbLjrW_iT39",
    "outputId": "b440efc6-5ee1-4cbc-d02d-93db44ebd956"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compressed pickle size: 690800441\n"
     ]
    }
   ],
   "source": [
    "statinfo = os.stat(pickle_file)\n",
    "print('Compressed pickle size:', statinfo.st_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gE_cRAQB33lk"
   },
   "source": [
    "---\n",
    "Problem 5\n",
    "---------\n",
    "\n",
    "By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.\n",
    "Measure how much overlap there is between training, validation and test samples.\n",
    "\n",
    "Optional questions:\n",
    "- What about near duplicates between datasets? (images that are almost identical)\n",
    "- Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I dont think doing number 5 is currently useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\npickle_file = 'notMNIST2.pickle'\\n\\ntry:\\n  f = open(pickle_file, 'wb')\\n  save = {\\n    'train_dataset': train_dataset,\\n    'train_labels': train_labels,\\n    'valid_dataset': valid_dataset,\\n    'valid_labels': valid_labels,\\n    'test_dataset': test_dataset,\\n    'test_labels': test_labels,\\n    'new_valid_dataset': new_valid_dataset,\\n    'new_valid_labels': new_valid_labels,\\n    'new_test_dataset': new_test_dataset,\\n    'new_test_labels': new_test_labels,\\n    }\\n  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\\n  f.close()\\nexcept Exception as e:\\n  print('Unable to save data to', pickle_file, ':', e)\\n  raise  \\n\""
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "pickle_file = 'notMNIST2.pickle'\n",
    "\n",
    "try:\n",
    "  f = open(pickle_file, 'wb')\n",
    "  save = {\n",
    "    'train_dataset': train_dataset,\n",
    "    'train_labels': train_labels,\n",
    "    'valid_dataset': valid_dataset,\n",
    "    'valid_labels': valid_labels,\n",
    "    'test_dataset': test_dataset,\n",
    "    'test_labels': test_labels,\n",
    "    'new_valid_dataset': new_valid_dataset,\n",
    "    'new_valid_labels': new_valid_labels,\n",
    "    'new_test_dataset': new_test_dataset,\n",
    "    'new_test_labels': new_test_labels,\n",
    "    }\n",
    "  pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
    "  f.close()\n",
    "except Exception as e:\n",
    "  print('Unable to save data to', pickle_file, ':', e)\n",
    "  raise  \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L8oww1s4JMQx"
   },
   "source": [
    "---\n",
    "Problem 6\n",
    "---------\n",
    "\n",
    "Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.\n",
    "\n",
    "Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.\n",
    "\n",
    "Optional question: train an off-the-shelf model on all the data!\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "regr = LogisticRegression()\n",
    "X_test = test_dataset.reshape(test_dataset.shape[0], 28 * 28)\n",
    "y_test = test_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>Note on the Following Training Sample Code</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code uses score which returns the mean accuracy on the given test data and labels. In multi-label classification (which is what question 6 is doing), the subset accuracy is a harsh metric since each label needs to be correctly predicted. \n",
    "\n",
    "Later on in the jupyter notebook, there will be code showing on which class the model is consistently predicting incorrectly. This is called the confusion matrix. \n",
    "\n",
    "http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>50 Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 46.6 ms, sys: 1.38 ms, total: 48 ms\n",
      "Wall time: 46.7 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "sample_size = 50\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr.fit(X_train, y_train)\n",
    "#regr.score(X_test, y_test)\n",
    "predicted = regr.predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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fdy0t30tf8v7kFcz9pHeOpvVwdaRd85oEhUYVyHxzlu7Au/90vPtPt7jjoNPpmDWqG90+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\na4qTBzMzM2vK/wFLZ9clqSRDRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4e00d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(9,9))\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "plt.title('Confusion Matrix', size = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+mzd1FCO/z2V4SuutyE4cpQbwkxbBvrGoB65LPc1/XXRSOYXW2/8cLR6re772\nf1+jbCfQGcADxd/le15F0od7LOdLNN9rue5PqL4gIp4Fvs3I88zXek1bL2l7YIvKQ2WZJ7V4+UW1\n/4eAg/roRLJR7f8GcGuPZZiZmZmZWR8cRDYzMzMzm1yHAH9k+A1+gH2BKyX1NKJV0kySPgOcw/AR\nfGWAYc+IuKfd8hHRAA6vPFQGHLaTdFw9ZWmbNswi6UfAZowcBf25AQexJl3xfi5jZGryb0gaNQAm\naRMyyFNNOTxanS+SgZt6YOgHkpbovvUcSzPQX66jC4vA05hExFPAMS3aeICkz3VThqS5gV8A9Xm5\nG+SISGuqp7SeDzio+HuozWvGpJiP+e8tnvqCpK62ZUlLkfvP64qH6svVO6K08mRtuZ4zJ0ykIuh/\nFCP3jSMlbdtNGZL2AvZieOD9GVpnsjgGeKLyf4PcPi6S1Codeav61gFOZGSg//cR0apj0PnAP2uP\nLQ98t5v6ijoXB77Qos6p1hHJzMzMzGy65CCymZmZmdkkiojngC3JQMwQw4MKawO3SjpZ0tqdypE0\nu6TdgZvJm+7VUcxlcPDLEfHTLtr0A+BsRga2PwBc06ktkjYArmd4Guuy/l9FxMmj1T+DOqvyd/m+\nVwYuLVIKjyBpAUlfBy4k08IO0Vta3qOAp2r1LgVcJ+k9nUayS1pC0jnA+xgeoGkAX+mhDaP5CnAt\nI7elQyX9QtIKHdq4DTn36kaM3JaOiwjPhzzcJWS6amh+XnNV/r4nIq4Zh3rPYWQwdFPgnCLdckuS\nFpJ0KNmJZqXKstXtcYgMdo7mX7X/55S0dVetnzzfZPjcyOWcxmdJ+qaklu9b0qskHQ0cx8j94tMR\ncX99mYh4hEx/Xw20N4ClgWslfULSnG3qe4WkQ8iRxXNXnhoCngP2b7VcESj/OiO3jT0l/VpSvWNI\nvd4tyc9nkdpTtzI8Vb6ZmZmZmY2T6T3Fk5mZmZnZlBcR/5C0EfArMt1v9ab7TOTclbtLegC4DrgD\neBSYDViADMCsRaZ9LeeqpFbOIRHxtR6a9X5yXt8VaQYoGsCaZCD5DnL0YJmSdTFgY2DZFm1okIGi\nnXqof0ZzKvAJcqRd9f2vBfxZ0uVkCupHyaDYm4G3kuuwDK48SI6w+yDDA2ktRcS9kg6iObKvrPfV\nwI+AoyRdSaamfryo69VkquG1yG2rvp6Oiojr+3j/7dr4fJEC93pyHtbqtvR24O2SbiGDRf8s2vha\nchT7YrSIkpcWAAAgAElEQVTeli4Bek37O+VFxIuSziazGFQDstPmQh+nqo8kO5jMy/D1tS2wpaTL\nyM4t/yGzIyxCboNrkJ1dqm18oHjs1ZXHuxlZfwewefF3udwZkk4D7iSDs89HxCA7SIxJsb52An5H\nvt/yM5gJOADYr/jsbiXnmJ6HnGd4I7LTSX2/OD8iju5Q39mSjgQOZvi5YR4yxfznJF0CBDmH8tzA\nCsAmxWvqwf0GcMAox4ujgHcB61Tqg+xk8Mdi37+enObgmaLOpYH1gCVb1PksmU1jSmWzMDMzMzOb\nXjmIbGZmZmY2HYiIuyWtRs49XAZb66M3FwG2a1NEu+DxQ8D+EXFOj+15sphf9wwyoFdvy+vIgHc3\nbbgE2D4inmCKKgJCewIXA3MysiPAJsVPVfWz+hcZBOtp9GREfF/SksAna3U2gAWBd7dZtHxNdZky\nED5QEXG/pDXJ0YOrMnJbehM5artT+8rHTiODSC/20IRu55meCk4ng8gw8n33k8q6m84MD0naA/gJ\nzXsM5fqbBXhb8dOu7DIgeSmwK5livbrdLihJEREdmvEzhncsKEf17ll57AkGO8q+na63t2LfWAu4\ngAzYVveNWcljwua1xVrtuxcAO3ZR3yGSHianLJipVsac5PGnfgxqtS++CHwkIlqlzq7W95KkdwG/\nJud872bfb1fnM8BOngfdzMzMzGziOJ21mZmZmdl0IiKeiohdyKDBDQy/kV5Ndd1KPRV2OTfmG3oN\nIFfa8xiwBfA54LEu2lJ9rkGOPPxIRLwtIh7voerR3msv+imnr7oj4lpydPGdjFx37eppkKPL14iI\nP/TThoj4FLA3OVqx1+2lQY6O3i8i9uhyhF/P6yci/kGOLjyKHE3Y7bZUfo73A++JiPcVaXK71c1n\n0UtZE6mf+q4kP6v6+76ttn11W39Xn12RJn97etsGKV77IPDBiNgsIh4EflOpv9RxnuCIuIjsBNGq\n3WXAcm5Jr6sv26Kuseh5e4uIu8mRuicBL9DdcaPcN/4LfDIitu12v4iIr5PnmD/S/XGqui/+Gdh4\ntABypb6HgA2BH7aor9s6bwDWjIgLuqnTzMzMzMwGw0FkMzMzM7PpTERcHBFrkYGFb5DpRV+ieUO9\n3c9/yYDkQcBrImL/iHh0jG1pRMSXyRSjhwF/GKUNL5E3/P8HWCIivtNjla3K7Fc/5Yyp3oi4kRxx\n92EyTWun9XYTsEdErF0Ekvpud0ScBCxHjki+pUOd1Z+bydS2S0XE97t8i32vn4h4NiIOJFOef5NM\ns92pfS+QQdG9gKUj4ifd1jXWtnZR1njr9zNuAGe2WP5HY6y/mxHJ55Op9Y8kMyB0WrfPkut2f2DZ\niDi+UtSPaQZTy3o/IqljJrWI2B04lJwnvN7u8vcqo7zXsRjLvvFEROxNfn4nkB0BOn1+D5HnhtcV\nQeGeRMSlEbEKsAPwC+DJUep7AbicHCm+SkRc1WN9j0XE+8kU5t8n5+4e7fj0NJm9YOuIWDMibu31\nfZqZmZmZ2dgMNRoT8f3XzMzMzMzGQtIryADDsuTco/OQqWIfJUf83g38PiKem4C2vBpYjZzHcyFy\nDtNHgbuAmyLi4fFuw4xC0sJk4GRRcv7qZ4B7gBsi4t5xrHdeMk3sMjS3lxfIbeVv5Hp6ZLzq75ak\nJch2LkSm326QbbyDbGMvI9htOiJpeTJ9+QLk9vckGTy8D7g+Iv47TvXORY56fz257T9PblN3Ab8r\nMixM9yStSM5LvxDwKpqf380RcduA65oZWJ2cf3pBct72Z8jP7U7yc3t2wHUuSaazXoRcT3OQgeMH\nyQ4mN0fEC4Os08zMzMzMeuMgspmZmZmZmZmZmZmZmZmZTeN01mZmZmZmZmZmZmZmZmZmNo2DyGZm\nZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZm\nZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZm\nZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZm\nZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZm\nNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2D\nyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZm\nZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZm\nZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNo2DyGZmZmZmZmZm\nZmZmZmZmNo2DyGZmZmZmZmZmZmZmZmZmNs0sk90AmzokvRc4tcuX7xERP2xTzhLAwcDbgCWAp4E7\ngdOB70bEs3207RRgt16Xq2nb5vEiaVngjuLfYyPiowMu/1TgvUADWCgi/j3I8ieapMWA9wBbAK8H\nFgL+C/wL+CPwS+AnEfH4pDVyOiHpi8Cni39Xj4jfT2Z7xkLSlsC2wLrAIsDcwCPAP4BLgXMj4rrJ\na+HUJGkW4EPAzsCKwKzkZ/5r4JiIuH1A9awB7AO8FVgUeAH4C3AW8O2IeHqU5TcC9gLWJrePRtHO\nK4rlb2mxzF7A8X009zMR8ZU+ljObVJJmBW4CVgDWiojru1hmzNdrxfH7g8CawHzAQ8D1xfIX9/le\nXupnuaqImPCOtuN5XpY0M/B88e8FEbHNoMqeLJLWB95FnhsWI7effwP/BK4Czu93G5pqJN1Hfka3\nRsSbJrs9/ZI0H7AL8A7gDcDCwIvkdX6Q1/lnRMRDk9bIKUrSUuTxfjPgteTx/g7gx8D3IuK/fZRZ\nPS714s6IWK7LOuYB/gS8BjghIvYZ5fVvBj4KbEBecz4B3A78BDgxIp7qo71mbU1P99DGek0m6RXA\nx4B3Ayoevhe4ADg6Iv4xWhktytwdOLnX5WpOiYg9x1hGz8bz3Fv7rrp9RJwzyPInmqQ5yPt47wBW\nJs/vM5Hn97uAX5H38f4+WW2cXkjaBLio+PegiPjmZLZnLCStAuwIbEqepxcAHiOPPb8Ffg6cFxGN\nSWvkFCVpa2Bf4C3AK8nP/DryeH/pAMqfiYw17AqsWtTxGHm/4f+AH0VE2+/skg4FPtdldetFxG9a\nlPF38nw4mr9GxOu7rAtJryWvLefmZXb/zyORbZBWLX43uvhpqbhwvRXYH3gdMBt5Abs68A3gRklL\n9tG2btrUV5snyGTXP92TdBB5A+lrwEbkRcjswLzAssB2wHHAHZLG2qFgKplhty1Jr5N0NfnFdG/y\nhuL8ZDBzEWA14BPAbyWdK2mRSWvsFCNpQeBa4GhgLWAeYA5yX9sPuKm4KTLWeo4iv0DsCSxd1DE3\neU74KvD74qZJq2Vnk3QacAl503mpYvk5yfPLB4rlD2tTfT/niefG9IbNJs8RZAC5q3PCWK/XJA1J\nOp48fm8FLEh2bl0MeCfwa0nf6vO9jPV6b8xB6DEaz/PyDHvOL0laSNL5ZEegjwFvorn9vLr4/8Pk\nNnSFpK6CTVPc9PBdZkwkvY8MWh4LvB1Ykjynz0We3zcnr0nulHTwJDVzSipuNP6R7Di4LM3j/VuA\n/wfcUNxQ60c/x+heAtbfIL8Tjrr9S/ocGTDbjdy+ZiNvaK8NHAX8QdJqPdRt1o1Jv4c2iGsyScsA\ntwBfLt7TK4ofAQcBf5T0tk5ldDCj3subiHpn6HM7gKS3kx3ETyS3t2XIc/ucZADoreT3lL9IOrLo\ngGQz8LqX9ApJJwO/Aw4h79stTB53FiDv6+0FnEPeV1pzsto61UiaSdJJwHnAluTxflZgcbID0MXF\nPbix1DEvOZjnB2TnwwVortvNiscvlfTKDsWswhi+txcdT5cYSxkdnEDek5xh98F+eSSyDdIqxe+b\ngfeP8tp76g9IeiNwJnlD4HHgK8CVZHBid7Jn2grA+ZLW6LHH82fJL7mtfBHYmjwA7A3c2G2bJ8h4\nH5imwo2lTwFfIt/H34GTyPX4L/KEuAR5w+m95OjkUyTNHhH9jDS06UAREL6YXLcvkaNSfw78FXiW\n/OL8ZjL4KHKk8rKSNoiIRyel0VNE0avwp+Tn2yB7uP+APG6vD3yK7Gl4kqS7I+LqPus5hgwElPv1\nkeT5ZQGyl/yWZMaBn0taJSJerBVxIjlKukF2MDmKvLkxCzlq/ePk8eAzkp6KiCMry54N3NBFM1cj\nLyIB/gB8p7d3aTb5JP0v8D89vH4Q12tfJG8ONMjz9TeAv5E3DT4JLA/sJ+mfEfHlHt/Sqh2eu7mo\n8wEya8lQj2WPt/G8JmvUfs9wJM0FXEiu4wY5MuVscqTgk2THwZXIINDq5DnpEknrRcTdk9JoG7Oi\nU9opxb//IkdfXQs8WDz2GmBjYA/yxvNXJc0XEZ/GxqQYJfQTMij1GBkguprc1/YAdiKz0Zwnaa2I\n6LozXUS8KKnT8bo0G3AG2ZnweeAjXbZ9U7LDYDcB5P2BQ4vXPgIcTp6b5iaDansX9V8g6c0R8UA3\nbTDrwvRwD21M12TFCORfkJ1MXiI77p9Jdq7dnAwizwecWbQhRnmfVefR/rpuW+Cwot3fB77X5nWT\nmW1vvK/rZthrOgBJbyXvK8xKZn44idx+7yczjSwMrEcey19FbkuLAu+bhObaABSdAM4kv4c1yAED\np5EjOx8jr+NE3sfZmOwc+itJG8/I2ROnI18hr98aZMe5bwJ3k8f7Q4DlgI9IeiAivtpnHaeRGV0a\n5Hr9OtkRdGnyHtyq5He0s8igcivlufFnjD4i+Y4Wj61S+fsDZIeFdrqOLRVZIDZjBj/29muo0XhZ\nvm8bB5L+RZ7YvxcR+/ex/BXkgeQZYN2IuLn2/EFkEKEBfDIivj72VkPRA2r3otyNIuLKQZRrE6Po\n9RpkZoVLga3bpWuS9BYy1e685JeaZV6uNwEqaTMbwFtmtAsyNVPUvwBsGxG/bPO6mclg4m7ke/32\noNPCv9xI2pv8ot4ADo+Iz9SefwNwDRlIviUi3txHHesDlxf/3gJsEhH/qb3mFJrrdZ+IOLHy3Lpk\nOtNGUc476scFSYuSX1qWIDsevL6XNGuS5iYDx0uRN21WjYi/dbu82WRTprA+hkxl1SADqg1g7U7p\nrMd6vSbp9eQXypnJYMQmEfFC5fk5yf32LfSxb3aiTHXdAO6OiGUGUaZNnEpqswbwoYg4rsNrv0R2\namoAP58KKbz7JeleckTZn2a0dNbFaIZ7yIDIreR3tZYBAeU0QJfRHHk6w13fTm8kXUOOxH2aPDf8\nsfb8IeQNyQZwYESMafRKmzZ8DTiwqOMTEfGNLpaZixw9vSTNc9uJrdJZFyNh7iVvXD8CrFz/flhk\nsTqlKOe7EfHhsbwns9Jk30MbxDVZkdXps0Ud+0fE92rPr0em350N+GVEbNXr+2xFzVTXDeCwiPjC\nIMq1iVF0TL+dDCzdD6zfLl11kQXtYjKg2AB2mNFTePdLzXTWDeDgGS2dtaQ9yM4CDeCITh3+JO0L\nfLd47a0RsfKENHKKkrQ8eW00E5nRabPqQIyiQ9CV5GCRZ4BlI+KfPdaxflF2GaRev3ZOmYnsBLxJ\n8ZptI+KCWhnlFEUNMmV7uwGBndpxABkgbwAL1u8l9kM5feafyHhCeW35WaezNuuRpHL+AshelL0u\n/2by4rcBHFe/+AUoLnh/R+6sH++/tTbFfID80gMZSGo7309E3EAGTofILzEfGv/m2aAVN4beQzEK\ntl0AGXKUAxkguZdc73sVF0fWv3LE4v3AiC/rEXFb8fgQsLKkdr0LOzm0WP554F1tLvoOIjsRAGxf\ne26v4ncZYB5xXChuEJYpL2cnR9P04ptkALkBfNQBZJuRKOca/w3NAHJ9JH+75QZxvfZRmtmQPlr9\nYlks/wzN0WOzF683A9iH3C6u6RRABig6OF1PbofvKDod2oxnZ/JmDcAB7QLIABHxVzJTSemA8WzY\nVFecJ9amGTj9Y/01EXEE+d1/iAz0DroN65HXnQ3gsm4CyIUjyWu0h7t47c7kiGPIue1GdDAu5qG9\njXyfO3bZBrOOppN7aGO6Jis6JO5XPH9LPYBclHE18O2iDVsUHY7NNiVTVwN8rtN8xxHxMJlVsBwF\n5/P7jKvszHXPaBljIuL75GjVIWAlSRuPd+OmuI+R987L+1fDvv9HxNPk8R4yu0VXmV9qtqj8/ekW\n55SXyPt4pa1blFEdRdzzubFWxn2DCCAXjicHynRzbTklOYhsg1JNMXNTH8u/q/L3qR1ed1Lxe2FJ\nG/RRj009Kxa/X+gyiPNDmnMezFCjMWya5ciUR5C9Vzsq0nadQV4szVEsb30oei8uT36WZ3ZIW3gy\nzf1shx7rWATYsKjj+HZfKIsvk4eTKaQvrD29XrF8FDeV27mo8nfXPVuLm5rlDZWLipuLZjMESUeQ\no/BXI7fh88h0790YxPXadkW9f4qIW1otXAQrbiRvGtQ7idjLkKT5gUWKf0c99xeqx2Zf882YVqz8\n3Spd3TAR8QvgoeJfr/Ox6fZ4f3LxezFJ6wyqckmzkGlxZyJHQO7d5XIbkp0JGuQN09E8To6KuZ9M\nm9jObcXvBSTN0U1bzEYxPdxDG+s12YY0A+H/16ENJ1b+7um7oU1ZvZ7f/0QzJe1K49Iimwgrksec\nTvdoqk6p/O3rurHZlvzs/xARt7Z6QdEZ6SbyeN/PsfrVlb/b7dd/otkhZNEWz4/13FiW0RjD8sMU\nGWm2IK9HDx7l5VOW50S2QSl7ebxIpkfo1brF7ydGSTtWTTW9ce3/CVGkx32++PfDZFqVb5M9tZ8n\nD5QHR8QVlWWWIQMObyV7281PHnweIm/knhIRl7aoa1maB95jq2l4i1z8x5MHxnnI/fnj5BeBpcmD\n/p3kXHFHR8STLco/lWaPvoWqvfsl3Uemvvt6RHyimFfqQ8CawIJkuq+ryfTAHddD8WV+f3I9L0j2\n3LmiKPv3ku4sPpcTWqUZ69IsxVxc13Z6UUQ8Wcyt9jQ5z2q7Ng9inb2dTLG9H7ArObfHi+TNzxMi\n4oTKcu8iP99VyN7wdwPnkOmCn+hQx/bkDY8DyXW5NM398FQydduw3l+9kPR2cs6MtckLgmeKun9O\nbpNtR4RIWpzsvbYZOXftbOR2cwtwPnBy9Da3eSvrdfm6E4Hfk3Pptb1gLVKe7wOsQ6Y5pnj9L8j9\n6MEOy65TLLseue88T6ZgvAT4VrtgpqT/A3Yhv5yvS8439z5y7qj7gR+3SBm9FjmCcAPywus5cnv+\nNXBMRNzXpq7q8euFiJit3ftpY93K35e3e1FEPCbpj2Rgttceo5uTNwwb5Dx8bUXE59s89R1y/f1r\nlLqq86F2dUNQ0hDwreLf58ljm9mMZM3i9yPk9copktrtS3Vjul6TtCSwOM1U851cSaZPXErSUp1G\nKIyXFtdaO5PzAy5Jptm6nkyp93xlmXeQ5+a1ybnc5iLn+Pobec14bETc36KucpoJgNWrn6+kq8nz\n0lkRsaOk1cnRQBsUdTxetOX4iDi/RdnVY/8F1dTOlbR4kDfl7iTP3TuQna5mJ88vPwO+ERFtj6uS\nZiODNzuTN4lmKZY9G/gaefPnquLl60XEb9qVNYq1JM1U9Gbv5Hzys3+IDj3ZB7DO/hkRi0lamuxd\nvwV5bn6EHPF/eETcVCwzD3m9vj15ffl80bZvRcTZHep4MiLmlbRc8f/G5HXZw+RnetRo18CdFFla\n9iNvMC1Pbu+PADcAP4qIM0dZflNyPs91ivf+X+Cf5HeFH4z2XaFL6wGnd/G6D5Db3oh1VpI0O7mN\n70rOA1fuRzeT18//FxEt5/0qlt2DDOCsQl6vPUqm2/4pOSJwxPVtn98hZyGzq7yb3H/mB/5DXkuf\nSX4faXmdXzl+QX/fscrj/aMR8YcOr6sf7/vdr+s+QrPj4hHddBYuUu+WwarjyfTmHUXEj4Efd9Ge\nJYvfj3fKfGXWg0m9hzaga7JuvxveJulhMuC8MS2yWU2E6vUUeWz9f8A7yWude8l7W8dWXj8veU7Z\njLxGWoD8vP5NBifOJs8XIzL6VO6n3RqVqSRq93K2An5ZtGVX8tppbuA+snPLN1od+2rH9+2rqZ2r\n9xYiYo3iuuF/ivewGHkf7BbgB8Cp7c51RVlLkwGTzYDXAk8W7/t7EXG2pOOLtt8ZEWPpqL8eeb0w\nmk+Q66DTtegg1tkREfEpSduQ3/XLe3T3kOfeI8v7q5JWJa/9NgAWIq/LLga+2Or+T6WOYyPio5J2\nJM93K5L3I+4iryW+2ele22gkrUBea2xETvMBuV1dSl5z/qXDsnOS95q2Bd5IZoR5lPyO8GsyO0nb\n+2KjKKdQWlnSXBHx1Civv5qcfvJf5HVWuzYvQW6LW5Hn67mAf5D3nY9p10mmWHYpcj1vRt5PnZXm\nNexJEXF5m+X6+Z74WrKD22bF62YFHiCv5b8bnaeUKo9f0OP3qOK4swjdH+9XBZaV9Jp29xbbqF57\nL08eV+teRzMddKtr9fLceE9EPNpD3cC076PLF/+OOYgsaWHyXNEgz10x1jJnVA4i26CUO/lfgOUl\nVU9WT5IXKf9H3kRodcPnDXTXG6n6/GSnwWmQabKuIeexAZiTPNjeWb5I0mfJ+dvKtBGluYufZYFd\nJR0TEe3Ssow2efmKZMBx8dprVyl+PiBp/R4P/o2yLElfJ294VctehLz5soOkz0fEF1sVouYcOdX3\nsQh5gt1B0seqdfXhJpopME6VtFtE/LbTAhFxRqfnB7jOXsXw0V6lNYA1JK1G3rA7mebcrqXlgEPI\nlE/rFKmkWtUxN3mSX7O2/DrFz3slbR0Rj3V6z3VFyujTaH62ZdmzAauTXyIPkPTeaJFOukhhfHbR\nvvp2sygZYD9Y0mYRcVcvbSNP2k+T+9tmkr5KXqSP6CgxbYGIoMPJvkjFdTTNNIjVNr+RvHH3QUnv\nql9EFqMRjieD+NVlZwdWIPfP/SR9OiK+1qL66vb/I/LGcvn/UsC0C+viZuK3aY7GqNZVtvMjkvaL\niHJkyCBVj7uj9Rj+KxlEXkrSbB1GLde9sfjdIAPrwLT3vji5X97Xqbzofk6+jSp/393lMruQ76tB\nh84BZtOxfwNHkDdAejo3MPbrtV6PIdXl/j5a48bZR8jsB+Vxd2FgzvLGgHK+tp/S/HJfPY+8iryJ\ntTqwv6StIuIqWmt1PVS9JvsY8HWGZ5RaEHgHmbb5pIj4wMgi2pZdfW5RsvPOCrXXLk+ug70lbdzq\nRkzx/i8k59GqL/sZ8jrn0C7a0VJE/EfSPWQHoTcAJ0s6sMhK0W6Z+8jzaksDXGdI2oLMejJX5eFF\nyXP6VpK2pHlTeOlKXXOQNx43kPTZiPhyhzo2AC6o1bEImV53B0ldzRfboty1yO8R5c2latnbANtI\n+gh5o/qhFst/l7zZSGX5WcmbRK8H3i/pZOADnW5Wt1G9+XO0pMeL0cZtRW1utRbtXZ68Ri2PZ6UF\nyPSam5LTn2zdoiPnquR6fl1t2QXJjqcbAQdK2i5apJYtdPsdcjmyI8RytboWKtq4GXktvl1E3El7\n/X7HKj+fTmXDOHw/LzpblHOa/4PshNKNw8nOGfeRgY+BTGEj6d00v89105HBrBuTfQ9tENdkvZRx\nF3msnMz7eOX11Gxk8HYdmsfI5cjOUwAU5+3/IzsK1Y+ji5HfS7cC9inuaTzdpq5ObZmbDOisX3vt\nMuR9oj0lbRsRF41cfFoZrR4rrxl3IIPFs1een53czjYCdpa0TavOSEUHu5+Q56eynvnJTgCbSPoR\nzY5R/aie3z9bBFdP69RBsF0wr9LmQa2zIUnH0cw8Nq0K8pr2HZLWJTuUHc3w2Mqi5DXvtpLWjYg/\nt6kDSUeT3y+qdbyBvA5/v6TNo82I0U4kHUp2OKzfz1yueA/7FvdwR1xzFsHYi8jrt/o10gLAWsBB\nknaKiJ/32jays94G5PXPWZL2jYh72r04Ih6nc6YFJO0NHENu29U2L03uS7tJOqTVNbKkjwNfIY8J\n1WWXJK/VdpV0BrBnm3uypY7fE4u69iG3l3bt3F3Sd8jpW1p1EGzUfvdiLMf7XuII55P7yBBwmKTL\nWryX6nbXqpPqKhSjiCW9lfyOsR7ZefZRMjh/QkSc16YNK5LfQxrAXyTtT8YfViK/d3XspNPC98lj\n3+/J6VLe0sUyU5LTWduglDv5UuSOtSfNHjzzkxcoJwJXS1qoumARGCgfa3vyACh6/ZYXdosPqO1j\n8XHyAuUr5EFtR+DLEfEPmHYyO4zc1+4he6i9jexxvhMZpHuJ/Ow+ImmjegVdOo+8KDqVDPqtRa6D\n24uylyADT/3YlXyffyd7TK1D3rj4fqXtn1fOyTOMpE+SAeQG2buqXP7tZFqlckTfEvVle3AcGWQb\nIoO710i6WdJhkjZUjhbo2oDX2THkzdQLydEK65O9J58plt+HHNmzG3kj6T3kunsfzVSNb6I5B20r\nR5AB5NvJnndrkSfI64o61iNP5F2TNBM50njrooyfktv2GuQNqyPIETrzAT9VLS2WMuXk6eRNzgeL\n9m9YtHOH4vMojxed0l61VFzsH0dzFOnBwP2Sfixpd2Wv6l6dTDP13V/JXpvrkqOJvkvOvTsfcG7R\nexCYNir1DJoj+u8iLyDXI497h5EXOjMDX5X0qTb1D5HbyvZkb8ktgU3IHm+nVF53ChlALnsQ7kau\n87eSF2v/JC9KT5C0S5u6GpWfXlWPux2P1zR7HQ6Rx6durVD8fiQinpG0jDJrwn/IUWF3Av+W9CNl\n7xJbzrwAACAASURBVOi+FOvuE5WHftXlouWo8GfIfcFshhIR746IT/fRuWgQ12v9HEPqy02WL5PX\nQu8jj/EHk3Ojl8pgZIM8nry3eN0mZKaRMnXX3OQNvX6sX9T5b/Lm0Abk8f8IMiNFg7zp9M4+yz+V\nvFlwAXk+WpO8NvldUfZ8rdpebBuX0AwgX1QsvxY5euAmcgTLiLkSe1T9vN8H3CvpPEn7SlIf5Q1q\nnc1L3midibyBtBEZ1D+reH528r3/kvwcjiGvp9YDvkiuO8jr6dfS2mxk4HMu8obLluR1yv+S1xlD\nwJHKURFdk/QmcsTMwuS13VeKtq1JXveeR/N68leqpfGV9H6ac6tfQl6DrklulwfQ3M/3oDkPXi9O\nJ79DDJHHnwsk3SHpSEmbF50euybp1eTIkjL4ci7NUei7kCOvG+S+9sPasiJH4JQB5PNo7ifvJNd3\n+b3r8lG2ydG+Qy5KjkpZjsyE9C1yna9BZp06lRy9uAJwaf37dUVfNxyL709lgHu04/2T5LYDgztW\nf4RmitzDuxn5W9zQ/zD5Xj9U7wDQK0nzSVpb0inkdlh+R/jcWMo1q5jse2iDuCYr/368i32uLGPB\nov2TZYgMJK5NdqDahLw/9V3yPIty3uZzyfkvnyI7721FXtdsC3yVDPQ3isc+2WdbjqI5CndXmueT\nS4qyZwd+oBwZ2ut7XIY8V7xAXptsXNT1v2TmjQaZAWzEvKfF8fRcMuDyLBk42Yi87jwMeIK8Zmp3\nv2FURUC4TJs7B3nOvUfSsZK2Le4pdW3A62wvMoD85+Lvdcn7LmWn81XIbedYciTlh8nt6R00s/vM\nS+fpgrYnP/vHyWv68t5Teb5ZlLyWaHd+b0nSl8jz1EzkcWXfom3rk/fl/kp+5l9oc2/qVDKA/DzZ\ngettZNBsK3IfeZ7soHVa0RmzV+W1/BC5/f1V0kWSPiZp5eIeTdckfYC8Nz0beS3yJXKf3pDsjPYf\n8l7ckZK2rS17ELmdzEpu018m95N1yE4c5f30ncj13UnH74lFO79XtPNO8hp5/aKufcjgeqOot9O9\n+4m8j1dfblQRcSP5PadBHs9ulLRbcT21k6QryWNcg8xocEl1eeUo4jLgvTE5cn5H8n7iLDQ7Tp8r\n6Zw2x8bqnMrfJ6+h1yH3ydlodtK5TdLund6PpPeQHWqfB/bqIgvWlOaRyDZmynQh5Y38uckv+t8C\nriUvOFYhU+6JPFn/UjmysrxhMj/NVAbdfNl7ivxSOd+g3sMYDAFfiIhqOp7ywnOI5oiLf5PpJqo9\neK4ne17dSDM1wg50kXarRRsWBt4fw+flvEHS+eSFz8LAlpIWiIhHWhXSwSLkCW3D2heDyyTdS7MX\n0e7kRQoAkl5DM4B8J/n+q2lnLpJ0ERmU7ftYFBH3S9qZvHEzG/l5vKn4+SzwnKQbyAvxn0fEDe3K\nGod1tgCZWvKDlcd+I+k5sgdag7whdDawY2V0xg2SLiM/tznIi96vtKljEXJf26ySCuYGSWeTFzrb\nAOspRwy3HY1T83HyC0KDPFGeUnv+UkknkYHvhcjRQK+vnFDfSe7XDWDr4kKidCNwjqSzyMD6mpLe\nGDnXUi8OIddxGcSfm7y42wmg2DavIG/2/Twi/tOuIGXK7l2K9l4FbFnrlfprSb8HTiAvPD5F3lyG\n/DJRBtuvALaqLXulpB8Uzy1B9sa7IFqnBRwiR0tvXjk+Xl5p5/aVdn4hIg6rLX+VMqXUleTIr+9I\n+nk1UFSkbZq53WfRhfKGIl2kHqo+38vxuvwy8qikzcn9o7w4LPeROcnAxpaS3jlar+Q2PknejAW4\nuX4B24qyR7aKdpzUx/HUbEY2iOu1V1X+Hq2Mfo8h42GIvAm3WSX7wLSsJ8WxqgxGnhkRO9eWv1zS\nicUyqwNLSlqjU8qyNm1YmOw9vWZEPFB57ipJt5KjbhtkwO7cHsouLQwcVruuvVHST8nz90rAGyWt\nGkV65sLHyCwSDTJlXLXz2/9n76zDrCraAP67Sy4dEi6hIDCgoAIqIbYgKiEgdmCBhYXY9RmIIKCA\nga0oKjZiBwaggBgoyigo3d2x7P3+eOfsPffuzW3g/T3PPnt3z5yYO+fMvOfNGcaY15G5vBt5wFo7\n0kgqby/zRxlkDe4KYIxZiayBXwAfWmuXxzpWPo9ZOcSgd4K11p+O8VMj6eNaEoom7RnhOf+DMWYd\nIleWIKSki6Q08vzcaq191Pf/H528Pxl5Rh82xrzlIjeS4VVkTV2IyPp+xdJPiNzbH5FbD0VkIL8R\n7RL3+3egsw1PD/m9u3dmIfJTX0SZkzTW2u1G0kl+6Y7hOY3e7H52G2N+QZRMnwDfJ1DwjES+xyBw\nvfWlLQWmO/n0C8QI3s0Y08ZaO81tf4pQWszrrLWRSr4Jzqj+PJLW8FViRyvEfId0PIvI2OuAkyKi\nmmcCHxpj3kWe8zrAMEQmzcZa+zzhdUhTIZW5GmS+rkQ+zNXOuHS1+3M1oZrL8fYpQ8hB+fVcRkf5\nj3cf4fe551R7jY2T0l9RkqWY6NDyQybzjpHsNfiPETOTSCEQACZZa/01nv3RvvcSimQ7K0rmtYlu\nvZiGGOt6u31SpRbyThlZ832CMeYTxMhWCzFypyrXVUMMlEdbqSfs8YMxZgqhNOd9EBkEyA4oeALR\n0W0HTrTh5TKmGGPGI3oT/z2UG3q56/AMVRnI/H81EDTGzEZ0bZ8CXyXIbJafY1Yd0W0e59N5/GiM\nmYVkCAAZk7mITJ6tazLGfIY4pLUCTjCxUzbXRp6BY6y1/ox5nzu5ZgjynA8ilIUuLk5Gvh35Dl4E\n+kbIRFONMc8h8tIxiG7qLWvtP27/hoSi4u+MkDdB5iGLyIQVEH3MKFLAWjvBGDPIXSeI7HuS+wHR\nAU1G5L4P40WKOsfAoe56VyL6W38U7WT3HE1BnDAHIw6AXl8HuX2XI2Ptz7wyzTmRfYAY0jsZY66N\nkBs9Er0n1iFkWP0U6BXhHDfNSMaecYjB9HJjzJs2onyitfaYWN9FEhTaO7i19gb3rNyLvDu8FNFk\nBVK+JVpAUXNC9oGKiCF/FGKTCCAOEdcjThZnIOVIzog4hldTOeCO8R7yjrwIka17IPaL0sALxphN\n1lcSwMM5cHh6+6E2Tkr0fQWNRFbyA8+DMogY2VpYawdba7+x1v5orX0aeYi9KK+WiAHIwx8pmkx9\nIS+FREoRpgWA5yEVK6qiAVK3YQOSaiFWCgj/xJkb7+0g8EOEARmQ1H+EoiDSCKWJTRbvxWRgDM/S\nZ3yfD4vYdgWhFGJ9o71wW0kr/QrhdUlTxikKjkBeuiKjLEsjXn33IIuzNVJ/OBr5OWYB5H6O5uH4\ntq9NFnCjjUjv55TDM1ybRnHOsQs4N1IwdcLipYQEgCtJAvfScAPy/U2IYkD2jj8PEfwCiPe036uv\ntu9zrPRaDyEvJzcjLzcp4V4gOiHjuomcHnn1EG/eV4Blxpjn4nhxekL5buAimzOtEdbaFwjVquro\n23Sz+70dOCfGvguQsQB5DgfG6dqLcV6OvHP9GsWA7J1rNaEavRV9580vvHk3mVrW/nQ/qczXXgr0\n6ki0VUnE47mhO45BvHqzEIXlOybF6HMjtYcedH+mUtf4evd7N6KwVZR9ifyQ11I5Rm7nkIIgiCiv\nYq1pTZBMFDsJOaOF4Yxr/nIauZX5/hdhQPZ4g9B6GimTJctiJDI2DLcu+SNxI4/vpeKbR2it8u+/\nG1FSplzXKsqxLkIcuVaRc+2viUR2jAEWG2PeMVLnLBr5OWZBpF51tHp+7xCSp7+y0VOv+f8XS+bz\n9o9U6GGlrt0d7jzVEcVoQpwTXXP35w02RjpBa+0oxEgdQFJ7+3UIXgrs+TZKfUFr7SLEqfMRcpkV\nyVo7E1FCfUJOOb8E8g5wC6JoXmgkq1AOjEQ19XT7fh1NEej6MMD3r1Pcvq0Rw3IQeD+KAdnb/0Xk\nPSEAtDJSczySuO+QRqKpTiPkNBg1LbaV+uevueOdE0fOzQ25me8D5M9cfTZiSAgCj9vk6g8/iERO\nrSYkq+WF+oTfawHkffIaIyVwFCWvFAcdWn7IZN7nVK4h8hhFxTNxtu2HGKWmRTFGAmClDvUcZH7I\njUwXQL6TATG2P+f7nBu5LoiUXpoducFaOwUJNgkAB0es68cha24QeCTCgOztPweR9zz5JldYqa19\nGLKW7SZ8fQ8gMkp/JEveUmPMbXHm4PwaM2+NviOKju13JBWw1+97bUSwgtPreWU10hAdRrRzBBHZ\nK0fJNSfr/eDanW+MSbY0w0C3z0okI0cOpzqnr/Lrpq71bfbr8WKVsnjW/dyN6CtTxlp7F+JAsIic\ncl0VxKHyMSRK+SsjWXOicTYSeQ4wINp7mpXgjRfc8Q90zp0ggTOesfJKG6UsiLV2BxLE4QVl3BLZ\nxpHoPfE6JDhoF3BxNLnGjdWVhAy8+SHL+Cm0d3BnND+KkONl5E8tpAxP0yi7+9fGCcDh1tonrLVT\nrLWTrZQHPBRxUAXoanJmYPSOkYXod3tZa9+11s6w1n7snHa6IoZ/gGeMMRWiXMuTyLxikVrI+zxq\nRFbyg8mIIqYz0M1auzaygZt8z0eUWwEkDbC3OPsVDqkIILkWVvKJILDARqkNBmCt/ddae7i1tqq1\n9vZobRzrCKWxy60w/Xmcbf6FrGIujr0TiaLMgZUoPE9hGXlsr5buAmtt1P0deU1t6F3LbGvt0cgL\n1oNI9IRfEPXul8ZIVMVrJiJVSj6PWRD42UZPF7rcd23/xDFWe/dWtAXNO8cnzkiZA/csfoA8c22N\nMcl4irYklHr4ywRtP/V99ivJ5vg+v2eipDq31v5ire1vrR0R6/oTYa3NslLHJQMxGI8nXKnsjXkp\nRFC2xph2/mO4l5BOru33sRSoju5APWttI7dvBlJvwzO4R50L3LV+jXjReWl7YpHjJc2dqzoiiAWR\naJt4fI/UjIbwcckPvPk61fk3lfbeS1I197mntfZ+a+0Ca22mtXautXYA4uwQQF40YtaQjMRIbaix\niAwURKK6on7vEfs1Q1L5B4E3c3vfKsoeTH7Ia3uqzAcx5mcQI5u1tpG1Nt0p1mLhj4zNrcwXtS6e\nU1rNJ+R1nRu+inRq8xFVnnSKHa8syWvRDInu+tYTbpDNNdbaZ5CakT0R7/bF5Fz7A4in+WwTJb13\nAYxZLJlpie9zrIwXfvkhlswHosyIxauEahN2jdPOz+m+z4lkPq8OcRUkusbDU8Z2MVJKJkdqQ2vt\naGvtHc4hL1dYaxdZa7sg75x3Iu+fXgp3/7hnAGOMMV+biNTbSJpIT2GYw/nWd65fEHm4kg1FCvtl\nt3iGBwgfp1OjbI/7DokYkD2SHZcSiOI/vyjKudpLrbqFJBwPjDFtCDnAXm/zJ0vMG4jTQDtEqfsX\n4qRyJ/CRSbFckqJEoTjo0PLzGAX5blhQxJPrTrbW1rbWtk9wDE9GyM2cEASm29iZQ/Kqx4MYMmPE\n8dOQKE0PvwzxUpz9x5FcBHpcrLXrnIPgAUi65S8QXUbk+u5F5c6MIWvk55hlEkMHSrhcF0svk4xc\nt5b4crGXhaMMoq+Ki5sbTkG+r8nWV4s3Emfs9Mrn+fVF/xB6ph83xnQ1EannrbXbrbX9rLWDktGh\nxLmG95EgHi+V/DxyjnkQyTw400jN+Ei8e3U70WvretyPBCKU9xl6ve90mbX2wzjX6Y1TAKhjjGke\no2m878KT6363EvQR61zrCTkPHBepr84jhSLXGWMM0gevhM1tSNBRaWS8b0fku65IpHikg8xYRMfa\nBTg/hsF9LWLc95wkboho0h3J4HGCjZGN01r7GRIUEkDmlrBsPkayQPZy57jMxs+CsM+g6ayVPOM8\nZuYRO+LQa7fOSIrdSxDjQEskRchmX7PIl/1opCMTWTLejgXNosRNQri0RQ2RNGzNkO+gAzKhBsm9\nY8f8ONv8329unvll1trMONs3I0Jt9rHdYuelNZyZ4Pg/Iwtavji1OE+zWUhduYpIOhavzo3n6RRA\narYtI0rEjJ88jtn8GNcYNMZkumNEiyby8KI94wkPU+JsA/l+z0OuswnxhRsIpf4AGGWMSSY9jVdz\nx2MCUkfwYCQt9k/GmCXIC8GXwBc2H1PBOQ/R190PxphDEMXPycjYey9FVZA0Rof5DPd13PYgvnTs\nMc4zP+JffgEyGQH6R2QMqhtj9rfRI8lizSn+cRlgjInlsRxJNM/XvODNJ8m8KPvrk6QyX3vRLEFg\nvLX242iNrLWjjTF9kXHoaYwp45QtMXHtn/Qd/1FrbbxaRX78qU5zW89UUfZk8kNeS+UYuZ1DCopU\nZb46yBzcGHkZPhKpDeWRG7knmMDZyft+c/uONz+JY0ce3193yl++IhrTkdpsecYpxz4glJbuIGTt\nPwlRonnp19KRum1HW4lAiUkex2x+jP/716VYMp+/Ta5kPmvtVpdisDkheTcRftlik0m+rHRDQmM9\nFDGUpiFRKXcaY2bi5D1gSjxFZqo45d9gYLAzErdHaqadQsi4HUAMqq8ikekeTXyfE8l8kSnrUpH5\nZiIK6BLEzgIVbz7xj8vvKY5LflEk7+fGmAaEnCYnxHDG9bcvjUQYpQETrbVv5OX8HtZav4P2dGPM\nK8j7TUdkjhlIKKONoqRMMdGh5YdM5h0j2WuIdoyiIpYjfw6c48iBiE7IIJFwRxPKHlIc9Xi5Pb4n\n162N5zRtrd1ljPkV0bflGWvtUiTl70hnuGyDGBA7Imu99x03RyKT20Q7jkcex2xlHL2C9/+gtXZF\ngjYQXa7zHAiiOl46/HJKMnLdQUiWtiDQyxiTbO3WbNnBWrvKSNm6y5HMfh8AG42U2vsS+Ny61Nf5\ngXNc/cL9eHL4cYgerzMSsRpE5KnHjDELIgy+XqmYPxMYzdcQqgvvyQ6N3L7JRFP/SMgo2gL4I0qb\nqHKdO9fB7lytUxiXikjq5ZiBKilSWO/g4xFH351AJ2vtVN+2hUht6m+Q7EFVkayCTT2bgxvHOYQH\nJuXAWvuXkbT8xyCZfyp7MqOV7ADJjOszhDKHnoxzAnXBV6ORMXvCWvtD9N33PdSIrBQ2/hfy+oQE\nYM+7pXyOPXLitcnhrVkEJEzD66LXbkaUGxkRmyM9rXLL5jjb/MdO1ZMpmODY/uP7j70fstAHSVDr\nxgmf65BUF/mKlRTcH7ufAS4V3VBEMPFS8g2NFP7yccyS8cyMZ6BPhqUJtvuNtbVjtgrh9+pM5b6s\n6n2w1mYaYzohddi8yI0MJJVlHyBojJmGGOKeT+CkkDJWUjbNBp4wxqQjEQQPIFGtVRDFj5ceppZv\n11QjF/z3bDLCnf8+q0Z0ZXKsOSW345LfdUS9ezpgjCkbzTPQh38+T2W+9j837ydoOxF5kSyDvPBO\ni9XQGPMwISHRS891RwrX5aVsX03iaHBF2RvJD3ltU5TtifaPPEZRkYzMdy5wGeL97E89531vySoO\nYpGjZEIE0WSyVMiNPOlfRxPVN4ylbMszzrg4D3jeGFMCqXX1MKKAKY3U5eoeuV8+jllBy3y7k3DA\n87YnI++ByBb+aI9k8ct837tI76cQWS+AGN+PRKINNhljJgKj81sJ42SQr93PXcaYxkiUUk93HT1M\neP3u/JD5diUybPrebfYjdr3IePNJnmXxfCCVudrfJq9ztf8ZTSZzwX2Ik+8GJM19gWCt3WGMuQj4\nD5E5L0WNyErhUVA6tPyQyTZF2Z7oGEHyobxFHtlqo6T69eMMCTcha4ohXPbxvvvdiN4rtxSUHi8v\nx/fWymRqVheIXOf0Q1Pcz4MuA9zdiCEvABxhjOlhrQ2rE52PY5aMTJdXmb646PFKRzjjX4O8b1yN\n2IwqImtzdwBjzFxkfX4snzJ/ZGOtXYJEuI9z5+qJ6G8bIGP5AOA3Itd0vwtbjxeNWHJdNUIBDKnq\n/auSf0bkAn8HN8Z0IBRM9lyEATkba+10Y8wIpBRPAyRSe0Ky5/HxG2JEDiBOD3Hl8yjX8Z8xZjPS\n3/q+TaORe2s+odrdCmpEVgofv/KrNGRHZS5GvFXqxdvZeZx7efUTLbqFQdxFwBhzOaJU8QyqQWSB\n+wvxXvoR8ej6F/d97CX4+5KMV2auhGLnXVjL/cxKFIVorZ3pjJtfIhGypd3v7NQn+Txm+WoczeU5\n/AJyMik4/OtCPyRqKBnCFNsuyvY0FxV8JpIysSWh+6Gt++lnjDnRpW5JCpfauRaQaa39O15ba+02\nYIQx5g9CNaVO9jXJyzqY6n3rH4tYLx2x5hT/df6PxMZVj/y+B/3eyPWQlEex8ObzLFJ7wfQb15fE\nbCX4PT5zpLWCbO/PsYRqRAaB2621Q5K9ICM1Nb3aUO8mUjooyt5IPslrkXNIPPzbi7XM5/r7HiHH\nKa8O07+IJ/WviCKsNqH0dHsLqch8uU7LZoypgqz9pay10SIAsnGRHS84h7WfkbIWYeUdCmDMClrm\nixet4uHJGcmmXCuJjMly5HtIdnzCorestRONMZ8iUSM9kBSBnhNmBeBc4FxjzP3W2vuSPAcuEqk2\nosj5N5Gs6KJjehtjXkWy8IDIfJ4RuShkvlTlPQi/zlYJ2vrJL0Wj5xC6DPn+E833FZCahPnxfn6G\n+72R8LI50c57GOL0G0RSrtYwOetC+2XDar60icvipBOPirV2hTFmKhL5foAxpoK1NpGztaLkBwWl\nQ8sPmcw7RlVjTLp79050jJXF4F0qkR7vSCQQobqv7RZEPvgTiXKbBDxK/FJVeyKeXFeQerw0RKar\nCSyPE9ELZEcpX2WMWUvIqHMyIsd5x8yvMQuy5+vxniV+CZQw/HpUZ8C/0RgzGNGfdEEMdV70aiOk\ntMNVxphOibL8+HGZImsh5UIS7metfdetu38gxtgWxpgaPqfK3Mp1RaXH+wi4K4Xz/pdC20QUxjv4\nkb7PHyVo+x5iRAbJKpAbI3KOtTGXxyjv7W+M6YJkIAwidoHGUTICNfZ9ru2TLRekotfeE1EjspJn\njNQ7bQDsZ60dk6B5Td9n/4vbbGSiSpSG6yDf5z+TvsgiwBhzOLJwpyEeMfcA71trF0W0K4F4NBeH\nujD5xRpC/Yl8mQ/D9b9yLs9zPxJVGkTS3CSMDnRKkZGI8RgknbF3LXvimCWqc+z//uOlzvbwe5qt\nd+nBc40vKvh/TgF8ApL2sDeSbucwJFIoqegBF1nsCY2TCY1jouv4whmSm+Mbc8L7m2o0vH/fmjFb\nhfBHwKQaqeFvvzWv45IHZvs+H0R8I7I3X89LMZXl74Tq2ySKqvGn1V4XudEYUx6JVvbqBO4CLrfW\njk3hekBenjzGp7ivouxN5FVei5xDcnOM4shDhOqQTUPkk++stWEOVsaYy4rg2goaf6RKXJmPGM4+\nSfK3238x4d7iMbHWzjbGfIbM4enGmCq+l/s9bcxKJ6Gg977/ZOQ9ENmiIVDBWvt7Xi7OKR0nuh+v\nJtrJiFHwRNfsbmPMJ9bamFlDIriCUE3cy5G0xckwnJAROZ7M5693nQhv31L+lHnRcM5r3rtNbiJz\n/fsssXHq5xUws4H9KaS52r0nHE0olXUi2fEIQvqs6wllGYpGAIlM6+n+vguJWscZng8C0q21kxKc\n0x/ttDc5gCuFTDHRoeWHTBZ5jHhOXg1xqWcTnKtIcUb3dxE9SxB4DHFU+cOl3/W3jVXvdk9mNTKW\niWQ6yL1cdwohI9ODiN4tGYYRMiL79Xh74pgVpB4vMx/0eCuQiMzRTq5pjzgJnoPUr64KvEJ4uY9E\nfIkYGncZYyolCgJy17HcGDMO8Goi1yGkD/Syvuwpery0YqTHi0du5Tr/s5XImOpfy7LtAS6aOQNx\nGo5az9hHjrXRGFMVMUrXRNKcxyy15JxVPX2jdz1emvwA8Ij7iUUAuS+9e/MCXBT93kq+1CBV9nnu\nQSI5nzRRXDQi6OB+ZxFe48GrLVXNpRKOhd9g9H1KV1n49CP0YnultXZUpDHSkZQibE/CpZf7G5lU\nWyZofii5d2jxG7BOTWE/v9e4P9JxTxyzwxNs97zBtgM2ieP5X/zaxmtojKlpjLnHGHORMaaF7/+l\njDEHG2PCxt5au95a+561ti9yX3gpVfwGurg45elS5OXgCBeVnCzeuPvHfB6hmjWHEQdjTH9jzH/G\nmK9cZKpfAIxbk8fhfZ8bEnnbRiGVcSltjLnXGHOpMSaZ60oFfx3AmPWPjDGVCaWySXWu9p8jbl+R\nmpUe8yOuoSzwCSED8ibgtFwYkCG09mQCUdPyKMo+Qp7kNRf5Nd/9maiGmneMhTZUx77Y4RzLrkDm\nu7XAydbaTyONkY7iJD/kF7/6PreK2Sq57fHwMo/UcVlOksVb+7d6BuQ9eMxiynwuusOrDRdZzzcW\nnmxR3hgTVwlojDnVGHOTMaanU9B4/69qjGlnjPEr2LDCE9bajki0qBf1kbTMR0jOD5I/cr5fEZZI\n5ptqjJlljBnl/pWKzHckoaiVuDXdYpCKzNfOGHOLMeYsY8z+uThXPLz5voaRVOGxyK/38/aEvrfv\nktwnmMRPtLZ+vkNkuzeNMYmikzzF6nZrbXEos6DsuRS5Di2fZLJk3w2bETI4Fnc9XndCBspnrLUD\nrLW/RxojHcVJRsgvPLmustN5RMVFE3uZulIlt+v7Ft/5/Ov7njZmAZLX40Fyct1cQvVrE+lQMMbc\nZozpa4w50fe/gDHmQGPM8f621tqd1tpvXCmwpsBPrg/NjDGJDJJ+vHEvSXh2wETEk+u864ipUzbG\nHG6MWW6MmWKM6er01XMJlWBJhP/7TEmuc+8W8wmlYY8rZxhjrjLGXG2MOS0JmSSV61hKKJNQsvP9\nv9baVBwu/SnYE90XfidPv0H5aeANpDxROvHx1saVvnXpICQjwUvEdy4EOArJVgVyT3vkRrYs6uwa\nhYIakZX84Fvf54tiNXIKn07IA/aptdZfM+Bt3+dL4pzrUvd7FRKFWJxp5PscL1XHhb7Pe1N2PEWM\nZwAAIABJREFUAK9WRX1jzNFx2vXJwzkmIkadAHCFMaZBkvt5CqxdSKpCjz1xzHrEWlyNMTWRNNJB\n4PMYytFIpiFR2AHgAhfJGYsbkFpkL7nzePyDKMDeyrmLYK2dj6QIDxBKjZMs77n9yrjzJ8RIHZ3D\nke8iO8rAWrsTmUsCwHHGmHj1ZrohXpdtkDR4SwkJrt3c9x3r/B0RgSYIfJHMNfux1i5EnAACwOnG\nmLpxmvdBaj8+R/z5NGWstf8iL5YB4Pw4wvqlhGSM92K0icVniFdpALjIGFMuWiN3b/ZAvtOZbjz8\nPE9IsFwNHG+t/SrFa/E4yp3nz2Q8ZhVlLyY/5LW3kee7dSxjoDHmUCTKLIhEFhRnahPyvP471lpr\njClFKK0+FL38kF/8hER0BoBzYik8nGNP72jbksS/lgxKZgcnH52A3Ef+95U9dcwujLOtDyEDXLLP\nzGe+z1fHauSM7k8hKSDHA+nu/8chkZlTEKN8LD72fU5F5vsOMfIHgDOMMUcluZ/fUP2N7/MkQkqe\n84iBMaYeojA8hFCd7M99TfolOL//u0xZ5iN8XBJl6hkGDAZeR2TU/CTZ+d7bthzIS91r//jGjBzx\nsNY+b60tEe+HUFr1IOBv759DPINWdULptHPgIkdbkktZXlEiKC46tLzKZN8TKlvUJ841XO77nOq7\nYWGTlE7IGHMsobSveamLXNzw15w9P0677iTOGhYVa+1cJDIyALQyxvRKctfTCTmlfeP7/544Zo0S\nyDV93O+1hPc1Kk6v9S0ukCfesY2U+BuEGOxu8W16ASnr8lUsfZM7jz8DZCpynf/Zv9dFOCeD52gw\nx5fKGiSyGUQujbl+I3JhTUS28yJkPbluf2NM16h7kV1n23sXWGmtTdZR048n19UgzruQcyga7X6G\nx3CCyAvefH+UMaZpjGtoSUjWSfUd3O8gFO+dBcLnFv9+3tpYCol6j4obs0bIdb7h2/QbMsZBoKsL\nbonFAN/nNwCstXcnIVu2d/sEAa99SWvtXh2FDGpEVvKHV5EIrwBwfbTFyhlX3kDuuSzgAf92a+2f\nyMIYAK41xrSPcoxbgNbIgzraSr2z4ow/9VhU7zo38d1ByJOlTLR2eyhPEard8bRbfMNwhrWryGVa\naOcV5UUIVEKEnXbx9jHG9PGdc2yE4WlPHLPqyHcdhhPIXsEp+oARyRzMGci81IE1gFecEjXy+McC\nN7o/tyAGOw/vxaOBMeZaomCMOZiQUTfZussejyC10gLANcaY4fG81Nz88w4i4O5C0hz68e6hkojH\nW7T+novUU/TuG8+Q6B0rHRgXzeDpPHifdX9mkeRYRGGY+10aiZaoFOVcTQkp17MI9S0/Ge1+1wNy\n1BV2Y3u3+3MOEg2cNC594WPuzzrAM06B7T9HGmIk9+aVJyO2n4vUYAwC24CO1tpfyAVO8PQ8lhMq\nNRVlbyaf5LVnEPkgADwXOW+6v59z23cRWpOKK+sJ1att4ZyWwnBr8rOI975HUcsP+YIbX29daIyk\niY7GU4jxNrc8DSxC7ouuxpjXosmWHkbSFb5OKAXdYN/mPXHMAsDlTnYOwyn473d/WhLXIfN4B6mR\nFgD6GmPOitFuFLIOBoF3fLLzj4RK2FwXJ2LpAt/nGUlem6eo/J/7swTwsZFaZTExxnR2+wSBr621\n2edzDnkTkP6eaozJoeByThDP+P71nNv3J0JOh2cYY66Mcf5LEPkDxKEyJRnInWsaEhkbADobY26O\nca5bEYVoEPjJWvtjtHa5xaVc9Pp8vYmS3cYYcwcheX6UzVud00Pd7x3ET4mb3zyNXH8AGBYtotvN\nEeMIGS/ipTdUlGQoLjq0PMlkzsjxFKEoO79S3jvGMYhzTRCYVITpXJMlGZ2QQRzp/f/LoUPYQ/mE\nkPP6bc6BJgxjTH3kfd2bO3PDne53ABjr9HQxMca0JvTO/w/hThR72ph539szRko5hGGMuZeQ48bI\nFNbWYb7Pr8SQb2sR0h8Ggcd9myf6PkfVWTlH/u7uzw3EL28WybuEopiPACY6x72oGMlw+CSSPSZI\nTifSZ5Ho6wDwqDGmTsR2jDFNCBnKrbXWM1g+jrwLBJCMEDkiZ40xZZC1v5I7f6QeMVn85xrp9GWR\n5yqHrAsBco5LfjEGmcfTkPk+TIfqxvY59+dOUqirDdmlDL9D+nCyMSZqJLAxpjvgydG/R5QSGYOs\ndwHgoWjvFu5Z9spAbME3Lk6X+LzbvyIytjlsn+7avICUD621v0a2UXJS1N7Uyl6AtXaVe7kdg3hr\nTzLGPIZ422QC7ZA0ZrWQB/QBa200o9G1wEzE0POlMWYo4h1UDriYkMf4X4QvjsWV8cDZyOQ1xC2O\nnyMvCw2As5DIRo8Aua8NXOyw1v5njLkfqXFyCPCLG9MZSOH6MxBP/hKEFsrcGJNvQSI8uwIHApON\nMV8BHyCpD9cBVRDFRG8kijQI/IJE0vrZU8fsIiNR2COBhYBBvKo8YesFa22yaeFAxux05DvrgYzd\n40gqvypIDZsrCdWFvjnCI3AI4lFdEXjcGZzHI8rfysgYXOf2z3TnSxpr7WJjTDfkBacsMo7nGGPe\nQjzXliFjUwepw3c+EnEUBC510bT+401w+/ZGXjp+cnPYn4ghvSvg1URcTshAirX2BWNMT7fficBv\nbt+fEe+54931VXHnfzAPSr7ngV6IN3o74HdjzAjECJ+OpKW5HvmOg8BQJ8hl44yxXo25TGttyvXc\nXJ8vRTzwbjCS4vBpxEv2GKROUWVEUL4qmgelMWYsIe/DC6J47Q1GPEaPQOb+ZkZqmc9BjNc3uPMH\ngS+stS/5jp2GKNO98z7r/h83dSWwKfLecPhTOEZGOyvKvkie5DVr7VxjzBCkJmUbYIYxZjCyZjcB\nbgOaIc/wwzGey2KDtXaLMeZjZK2oAHxnjHkEibAog3hzX4GszX5lW3GQH/KLRxHD2SGIwvEwZO5d\ngshP/QnVOvX6n5LM577nzsg6X92d71RjzDtIJMISZH2rjaxFFyJreBC4y1o7OeJYe9qYBRGZeaIx\n5glEzs0EOgI3If3IRMqxJKolC0gdY2PMRcBXiF7gdafYeQOJLGuAyHteWYg17lze/juMMQ8iisb9\nEPlpJCKXrEPksLMIRV38QbjSN5lrHGUkDWo/xHFsgjHmR8QAPhtRHFdEjP09kPSIQUQejhYFcR2S\npq8a8JIx5mTgTde3Joh84UVgvGit9UfWXoooPyshSqlOwFhEvs1A5Bqvr1uBc/IQRXIp8s5UEXkn\nOQF40fWrjuubF3WzA+gbeQAj9bw9J8bnrJSTSZVr3HWkA1+7ufsL5H7rQyhK5A+iKJxTlDubuN+r\nCtNZ3Fr7szFmOPLudCDwh1vXpiHXfhwiX++H3BcPRdwXipIyxUWHlk8y2SOIs1AjYKgx5nDEmX0r\nojcYiKytWxF5oLjzITKflUWcht4BXkb0ALWQ9/4Lke84UkYoqhr2+Ya1NmiM6YfIBuURGWkk8Cky\nJx6D3Jv+smIpr3VOB3Mb8DByf7xgjLkJyWb3M5LithxSS7sLYrhMQxwBe0Ssr3vimAWR8l8/u2fu\nVyRa9jKkr1798MExjxCBtfZLY8yzSOR/E0K6Kc+wdxQix9V2xx9vrfU7u72HfPetgF7GmGmInucf\nRAZtishRTd3+g53DX7LXF3R6vO+RcT0Z+MsY8wFyfy1E5gkvavgCJMtKEJFjXos43krnuPIE4uz4\nizFmGOIAVwZ59xhASEa+2rfvP84RbjAiV8109/lXiGH6cEQm9N4FJllrhybb14jrnGOMuQfRe9YE\nphtjRiPz/Q5E73oTocjaqYSMpNkYYyYTioLtYK1Nqcyatda67+c2d5zp7v3nb2Sev41Qfx+01i6I\ncg2JZMu+iJNpFWCEEefXl5GU3jUQfebFyLO8lVCmDO8aZxljHkX0/LWRNelR5B4uhdwzNyBjmoXo\nGyNLUD6A6O0bI++LBzp99r/IWPch5AixgMRZfxSHGpGVfMFa+6yRNHVDkYX7dvfjEUQEjgettQ9E\nOQTW2j+NMT2QF/kKiKHmbl+TILJ4nWalLmqxxlr7njHmeWRSLIUsXpGemUEkZUhNRJHV0BhTOpWF\nuIBJ1qswajtr7SDnQdsfqIsYOf3sQBaqR3x/p4S1drcxpjcSHTwQUXKcRPQaG56heixwo7V2S8Sx\nCnvM8qPGxbtAcyRtr7+2hdfXMcjLZdJYa7cbY05CBPjjEYEiUojxnuk7rLVjIvZfZCQl0VuIou1M\n9xO5/2agX24UMdba75xx+gmkjkkt5D6L9mIaRBR811prP4yyHeSlYhciZDQnPLLaO8a/QDdrbeSL\nRi/X/lxEGI6M/g0inny3WWsfI5c4obsHokQ8C3mmIr0hvXF/3Fp7OwVHd0TQb4U4HPjTmQeRZ/mK\nJJwXor5wWmt3OcXueMRofjjS78h9PyZnSsqTCK/Bcp37ScSX7lyR+FM5rY+yXVH2KfJJXrsXeTG8\nDFFEvByxfxCpafa/KPvmlXyrL+XjGkQRdABieIu2Zq5DFASed3QqdX2LNdbancaYUxDFdTNEWeeP\nBAkiCuzvCKUCzo3M95eRSKuRiGK6MnIPXRalufed32KtjVzTYc8cs2HIenYD4Y6QQcTh8aw4624s\nWf17Y8zpSKRFNUSWOTeiWRBRsnS31i6J2P9xF71xDZLW8r4opwkijoin5yZS1Vp7tTHmTyTCuApi\n6IhW78+bOz4GrrZR6rg5R8TjkYjkAxD578Iox3idUJSEt+9cJ3u+hxgbzyBn+kRv/jvbWvtXSh0N\nP9ffRtKFv4coRiOfKe9caxBjdbz0irlOh2it/d3J9K8jxoR73Y//2BboYvNe7sOTtwpd1rLWDpTA\nFm5C7rHIaKcgooC+21qrUchKvlCMdGh5ksmcQ1EnxMGkIeJQ409V6q1RvfMyLxYW1tqlxpirEENJ\nCcRBqUdEM8/QMxm41f3vEMLTlEPByJwFjtO1XIREJaYjOrvbfE2CiCzWHVkPczX/W2uHGGMWIs9A\nBvIdNo/S1FvHfkB0DGH30R46ZhYxEvdADLV+gkhfz0jWMdDHlYQcNqoRkcGA0DM9HjHmhS7I2iw3\nn3yKzAVHEF0vloVESOfISpcIa+1yY0wHRI91DnJ/RZM9vXNtc32IuvZaa59yEcOPII4ND0c5xhYk\nmOSbiH2HGmOykDW/IuJMc1fEvkFkTrwm+V5Gvc6HjTG7XV/SESOpP5W4d65vgJ4x5OVgxO/ccCei\nO+0DHIw4/ERew1PW2liZpSKvJQwnv56EOHseAJzmfiL3XY68t+RIP2+tvc05Id6E3MOxxrR/pGOB\n23+j0yW+j+gR2yKOWZHH+B1xSFkWr6NKCE1nreQb1tpRyII/GlEUbXE/fyNpEFrHEn59x/gUmcge\nRxbVre4YPyNGwpZWUpHlN6lGwSY1eVtrr0AWxi+Rl/xMRID+EzGItHdtPnLHKk3IIybyXHm57njt\ncrstqXbW2hsQg+47SJTIDiRSdCwyofvrLGxO4lzRzrHLvdQ0Qhb3CUjE4mrEeLcc8dAdgtyHfay1\n62Icq6DHLLJdoraJ2ixDUlQ9hDxr2xAvr3eAY621V8dR2MUbt7XW2pMQ5dhbiPJwG/JMzkFS4Bxu\nrY2aFcBa+yXixfYA4sm/FnkJXoNEcTwINLXWvh6nb3Gx1s601rZFDH9eRK7nvbgZmItE6lwCHBzH\ngIy1dqe19gLEAPm6O852ZOynIy8ah1lJGxa57w637/FICpr/kO9qLTJ3DXJ9jWdATupZs9Zut9ae\n6871CjDP9Xc7YuR+BblHb4p5kNC58qJUXIMIY9ciL2JrkWdtAeJk0cpa+2qCw8S9BmvtJmvtqUBP\nZBy9+WMRoiDuaa3taq3dFLFrK8L7mMpPNCr5tqsRWdmbSXpeyKu8Zq0NOs/l05DnexmyRqxC1tfT\nrbUF4RWcm/kvYXtr7WIkenEQ8kK6lVB/piAK3abW2peRNRGkvn1kHbGClMniya6pyC1RsZLiuCXi\nfPcjYoDdhkQo3oXIKn4nrNzKfHOttach0QWDkO93PrJeb0PW4M+QaIPGMQzIhTVmqbTxt43FG0gU\nyVtIhM4WxDg7CGhirf0szr7xZD5P8X87Eh2yGvku1rm/BwDNrbW/x9j/OiRacywii25B1uvFSGrE\nSxC5YEm0/ZPBWjsacRC7FDGW/Il8BzuRMfsNcSw83skGkVEJ/mP9gTg73IAoj/3vCxOQ+ecCa21m\nlH1nuX37I9EqKwnJJl8gSrnD4hh1k1YAWkmtZxBZ6wt3fTuRki4zEMNPM2vt1zEPkg8KR2vtR8h8\nPwoZX0/OnokYFVrF+75Jft6tSMh5Iz9J9r19IPJ8vYzMI957wGxkrWuqBmQlvykOOrT8kMmstfMR\nx6zbkPlpAzI3/uP61sJa+3nMA+Se3Mxtych1LyORem8ia9kuQnqWDxCH7mMR/ZB3rGglIRLJZMlc\na7xtBanHex2JkHwa0atsQ+bnz5B74kYk8CJILmU6d543kKjZc5H5dxZyD+5AdAyzke+5i7X26Gj6\nGHecgh6z/G6Taa09E8l8Mx35DlcAkxAnjGNtzuAF/7Gj4p7nGxGdyBhkXtlESFZ5G+hsrT3XRgmE\ncev54Yhe9UtCsscmZH4ZA7RJoG+Ki7V2hbX2fCRz4t1In+cRmjcWIvLZQES+HWzjZHZxeraDCc2j\nm5Gxn4PMi4dYa9+Kse8wxGD+OPLOshGZP/9C7pWjrbWX2thOOKm8Pw9x5xqB3OcbkPt0OTLXnm2t\nPclauyHOYfKqxwtaay9Dovs/dOfehcizE4FTrbWJgpDiylVWysgdjDi+fo2sJZ4ueDJiPG9ifVmi\nohzDk8leQvSc25Gx+QPR6x9ifdkIo+y/yO1/GSJDryIk63+FREy3ttb+l6Cvscj1GOzJBILBfbLf\niqIUE4wxRyLKuSBwvVMSKXFwER//IN/ZE055pyiKoiiKUmxxaZivQuSXytbaXCsd9xWMMQ8gUQNB\n4MhoHvuKoiiKoiiFjTFmCxJF/54ziCoJMMZ4JTBmW2sPLerrURRFSRZNZ60oSoFgjBmIRHhYGz8d\n5Um+z/FSsSmKoiiKoijFDGPMJ0gmionW2olxmh7vfs9XA7KiKIqiKErxwtUwvQqJDH0sVhYRY0xb\nJC1vENXjKYqi7PVoOmtFUQqSc4B7jDFdo200xjRB6hyApKyZWlgXpiiKoiiKouQL9ZG0YKOMMRWi\nNTDG3IikAQ4iqfQURVEURVGU4sV6pKTZTYR0dWEYY8oBXlmzIPBe4VyaoiiKUlRoJLKiKAXF68A9\nQHngdWPMs0hNjZXAfkAHoB9QDRE8r7TW7i6ia1UURVEURVFyx7PAcOAA4CdjzCikht0OoB7iVHiG\nazsfiFvfUVEURVEURSl8rLUzjDG/IfVqbzTG1EfqDC9CdHvNgauROsZBJFr5j6K6XkVRFKVwUCOy\noigFgrV2sTHmHGAcUAG43v34CQJbgasTpD9UFEVRFEVRiicjgYOBy4DGwKgobbx0h701lbWiKIqi\nKEqx5UzgY6AR0Mv9+Am6n1HAbYV7aXsFgaK+AEVRlFQJBIPBor6G4kqw4U0fF/U1FBr/Dj+N6he/\nXtSXUWiseflc0lteW9SXUWhs+2U0AOmtriv0c6cFd1Bx1yLK7l5NyaytpJFJVqAUmYGybCtRky0l\nM9idlp6v59z280igaPpbGJTM2sr+274HYHPJ+qwr04xtP48kvf0dRXxlhce2qYM4aMAnRX0Zhca8\nYadS75oPivoyCo1FT3QHoFyvF4r4SgqHre9cCkB61yeL+EoKj20fXl2YL8/BeSu3FeLpipaDaqaT\nfuJDRX0Zhca2r+/ca9f7aBRnGafM7nWUz1xCmd3rKRHcDsDuQGky0yqwpWRttpaoDYHUqill93cf\nkduzZfaTBgFQecMvVNr0OwDLa57OrtLVi+zaCoptX91BeutIP9O9l20zHyf91BFFfRmFxrZPbgQg\n/Ygbi/hKCodtP8nYph97X9FeSCGy7bv7ClWm27QjqxBPV7RULJO2782P2t/iQTCLCpmLSc9cSems\nTaQFd5JFSXanlWFHWlW2lKrLzhKVUzrktpmPAxTfPucz2f31yewZW7+lRHA7u9IqsDz96KK6tAJj\n288j95n1HmTNT28zsKgvo9DYNm0osA8+w0fdXMRXUjhsm/4oxHFy0UhkRVEKlKxAGTaUbsQGGhX1\npew1ZKaVY1H5U4r6MhRFURRFUbLZUaIqO0pULerL2KvYULklGyq3LOrLUBRFURRlXyKQxuZS9dlc\nqn5RX8lexdJyxxX1JSiKouSK1FzBFUVRFEVRFEVRFEVRFEVRFEVRFEVRlL0aNSIriqIoiqIoiqIo\niqIoiqIoiqIoiqIo2agRWVEURVEURVEURVEURVEURVEURVEURclGjciKoiiKoiiKoiiKoiiKoiiK\noiiKoihKNmpEVhRFURRFURRFURRFURRFURRFURRFUbJRI7KiKIqiKIqiKIqiKIqiKIqiKIqiKIqS\njRqRFUVRFEVRFEVRFEVRFEVRFEVRFEVRlGzUiKwoiqIoiqIoiqIoiqIoiqIoiqIoiqJko0ZkRVEU\nRVEURVEURVEURVEURVEURVEUJRs1IiuKoiiKoiiKoiiKoiiKoiiKoiiKoijZqBFZURRFURRFURRF\nURRFURRFURRFURRFyUaNyIqiKIqiKIqiKIqiKIqiKIqiKIqiKEo2akRWFEVRFEVRFEVRFEVRFEVR\nFEVRFEVRslEjsqIoiqIoiqIoiqIoiqIoiqIoiqIoipKNGpEVRVEURVEURVEURVEURVEURVEURVGU\nbNSIrCiKoiiKoiiKoiiKoiiKoiiKoiiKomSjRmRFURRFURRFURRFURRFURRFURRFURQlGzUiK4qi\nKIqiKIqiKIqiKIqiKIqiKIqiKNmoEVlRFEVRFEVRFEVRFEVRFEVRFEVRFEXJRo3IiqIoiqIoiqIo\niqIoiqIoiqIoiqIoSjZqRFYURVEURVEURVEURVEURVEURVEURVGyUSOyoiiKoiiKoiiKoiiKoiiK\noiiKoiiKko0akRVFURRFURRFURRFURRFURRFURRFUZRsShb1BeztHNt0P+7ufjBpgQDjpy9izNf/\nhm3v1iqDfic2BGDL9kzueWc2c5ZtAmDw2S048eCarN60g9MenZy9z6mH1ua6UxrTqFYFzhgxhdlL\nNhZehxJwYov9GXReKwJp8Nq3/zLy47/CtjeqXZFRl7fh0AOq8eDbv/HUZzZseyAAX913CkvXbeWC\nx74H4JB6VRjW50jKlCpB5u4sBr78E7/OX1tofYpHx/bNGHpzL9LS0nj5/akMe+nLHG2G3XImnY4+\nmC3bdtL3nrHM+ntJ3H17nHw4d/Y7jaYNatPhgiH8OmdxofYpHh3bN2PogJ6kpQV4+f0fGfZylP4O\n7EWno5tJf+99Lby/cfa9/oITGHRDd+qeeAfrNm4tlP4koiD6e0ffzlzaoz0r18pzfu8TE/li6l85\njltUdGzTmKE3dCEtEODliT8x7NXvcrQZdmMXOrVtIn1+6B1m/bOM0qVK8OWTfSlVqgQlS6Tx3qQ/\nGPTC1wA8dHVnTuvQlB07M/lvyVr6PvQOm7buKOyuReVYsx93ndFM5uhpi3lmUvgc3bXl/qE5ekcm\n97zzJ9bN0Q+f1Tx7jj592JTsfW44pTEnN69JVhDWbNrBwDdmsXrTzsLrVByOP7gm9/ZqTlogwBs/\nLOCpL+aGbW9YswLDLmxJ83qVGTLhL579el72tstPaMg57Q8gKxhkztKNDBj7C7t2B3niktY0qFkB\ngMrlSrFh6y5Oe+TbQu1XLDoeXochl7aR+/mrvxn+/u9h2xtnVGLMNcdweMPq3DtuJqM+nJ297amr\nO9C5dT1WbthGm5vez/5/lfKleeWmE6hfowILVm3iwmGT2Lh1V6H1KR4dW9Vj6BUdpL9f/MWwd37J\n0WZY3w50al2fLdsz6fvYV8z6bw0A/bsfysUdm5GVBbMXrKHv41+zKzOL5gdWZ9TVx1GubEkWrNjE\nJcO+YMv2zMLuWpHz07QpPDNyKFlZWZzSpQe9z78kbPs7r7/MpC8+JkCAzN2ZLFrwH298OInt27bx\n6EN3sX7tGgJpaXTu2pPuZ56Xvd+Et1/no/fHk1aiBEe1O4ZLrry+sLsWk45HNmToNR3lfvrkN4a9\n8UOONsOu7USnow5iy/Zd9BvyIb/NXQHAnHHXsGHLDrKygmTuzuKYq18M2+/63m0Y1O8k6vYYzrpN\n2wulP4koiDX/lYcvplH9mgBUrVSOdRu30v78oYXXqTgUlEx31dnH0vesDmRmZvHp5NncPerDQutT\nPApCZn/o+u6cdmwLduzK5L/Fq+l776ts2lI87mdwz/DVHUlLwz3DP+ZoM+yajtnPcN8hHzJr3koA\nKpUvw1MDTuPgA2uQFQxy5dCPmDFnKVUqlGXs3WdQv1ZlFizfwAUPvMfGLcVDpuvYrilDb+4pc9YH\nPzLs5a9ytBk2sCed2h/Mlm076HvfuNAYx9i3ReMMRt1xFmVKlyIzczfXD36Ln/9aVKj9ikXH1gcw\ntN/xcs2f/8Gwt37K0WbYlcfT6YgDZXyHf86sf1fRqE4Vxt5+OsFgkEAgQIPalbn/lak8OeFXWjTY\nj5HXnkS5sqVYsGIjlwz5hC3bi4mM064pQwecIf2dMI1hL3+do82wm3vQqb3MWf3uf53frIzvU3ef\nzakdDmHl2k0cdW5oDr67X2e6HNecrGCQlWs20fd/r7NizaZC61M8Oh7ViKH9O8s8+9HPDBs3JUeb\nYdedSqe2jdiybRd9H36fWXOXZ28LBAJMebYvS1ZtpPftrwNwR5/juLRLa1au3wLAvc98xRfT5+Y4\n7t7O1MnfM2zIwwSDWXTr0Ys+l14Rtn3z5s3cffstrFi+jN27d3PBxZfQtXsPFsz/j9untAOCAAAg\nAElEQVRvGUAACAJLFi/iqmuu45zzL+RvO4eHH7iPHTt3UrJkSW678x4OPqR5kfQvGqnOj/3+N873\n/JzLqce45+ecR7LbV6mYztjBfahfuyoLlq3lgtteYuPm4rEGFkR/e5x0GHf2PZWmDWrR4aJhxUtP\np+Obo02q/X3oum6cduwhorNavIa+/3uNTcVFvikAmb154wxG3XE25dJLs2DpWi6582W2bCseOivY\nB9f8toahN3aTcZownWFjv8nRZthN3enU3siaf/+bzPpnKXVqVua5e8+hZrUKZGUFefGDaTw5PiQv\nXNX7aPr2akfm7iw+nfIXdz/5SSH2Kjb73Bzd1jD0pu6h8X1lUo42wwZ0p1O7pmzZvpO+//ON733n\nhsb3/Wk8OV7sg1UqpjP2oQuov39VFixbxwW3j2VjAb+HaiRyARIIwH09D+HiZ6ZzypDv6Noyg4Y1\ny4e1WbhmK+eM/pHTH53M6C/n8tBZIUHz7emLuXjM9BzHnbNsE1e9OJNp89YUeB9SIRCARy5szZmP\nTuLoOz6mZ9sDaLx/xbA2azfv4LZXZzL6k+hGs36dDH8vDTeK33f24Qx+73dOuOdTBr/3O/edc3iB\n9SEVAoEAI249i27XPEGrMx+kd+cjaHJgrbA2nY4+mAZ196NF9/vp/+DrjLrznIT7/vHPUs4e8Czf\nz/yn0PsUD7nmM+l27ZO06j2I3p1b0eTAmmFtOrVvJv0940H6P/Qmo+48O6l969Sswoltm7Jw2bpC\n7VM8CrK/I1+dRPvzh9L+/KHFyoAcCAQYMaAb3W58kVbnP0bvkw+lyQE1wtp0atuEBnWq0eLs4fQf\n8j6jBnYHYOeu3Zxy7XO06zOaNheP4pS2TTiiWV0Avpz+D63Of5y2fUYzd/EaBl50XKH3LRoyRx9M\nn2dm0HnI93RtuX+OOXrRmm2c88Q0ugybwhNfzOOh3r45esYS+jwzI8dxn5n0L12GTaHb8ClM+msV\n13VqXOB9SYZAAB44qwUXPPEDJz34Nd2PqMtBtSqEtVm3ZSf3jJ/FmC/DFUq1Kpelz/ENOXXwN3Qa\n9A0l09Lo1lrG95oXZ3LaI99y2iPf8smvy/j0t2WF1aW4BAIw/PJ2dHvgM1rf8C5ndWhIkzqVw9qs\n3bSDAc//yGMf/JFj/1e+/ofuD3yW4/8DehzKpFlLOfy6d/j292Xc3POwAutDKgQCMKLfsXS750Na\nXfMGvY9tTJO6VcLadGpdnwa1K9Oi3zj6P/ENo66WZ3H/auW4qksL2t3wFkdd9yYlS6TR+xi5b5/q\nfzx3vjSVNteNZ8KP/3JTr5aF3reiJisri6dGDOaBYU/y9Nh3+PbLT1i04L+wNr3OvZjRL7zJqBfe\noE/f6zj08COoULESaSVKcMW1A3h67LsMf+oVJr77Zva+s36ZwbSp3/LkS2/x1Mtv0/Oci4qie1EJ\nBGDEdafQ7dbXaXXpM/Q+8RCa1Kse1qbTUQfRIKMqLS56iv7DP2bkDadmb8vKCnLKja/Srt/zOQzI\ndfaryImtG7BwxYZC6UsyFNSaf9HtL2ev9+9/9SsffP1bofctGgXV32NaN+K0Y5tzxFmDOfLswTw2\nNqfCpygoKJn9yx/n0OrMh2h7zmDmLlzJwEs7FXrfYhEIwIj+ndwz/Cy9T4j2DDeUZ/jip+k/4hNG\n+Z7hR6/pyKfT5tHy0mc46ornmLNwNQA3n9uOST/P57A+Y/j21/kMPLddofYrFtn35TVP0eqsh+l9\nSpx7useD9B80nlF3nJVw34eu78YDYz6h3flDeWDMJwy6oXuh9y0agQCMuPpEut31Lq2ufIXexxma\n1K0a1qbTEQfSYP/KtLj8JfqP+opR/U8CYO6S9bS79jXa9x9H+/6vsXX7Lj6YKnLfk9d35M4XvqfN\nNa8yYepcbup9RKH3LRqBQIARt/Sk27VjaHXWI/Tu1IomB0SOb1MZ356D6D9oPCNvOzN729gJ0+nW\n/+kcxx3+yte0Oe9R2p0/jE+n/MUdV5xS4H1JhkAgwIgbTqPbzWNpddET9D6pBU3q7xfWplObRjSo\nU5UW542i/6MfMmpAl7Dt157ZhjnzV+U49sjxP9D+8jG0v3zMPmlAzsrKYsjDDzL66WcZ/+6HfP7J\nx8z/L9yJ+K03xnFQo0aMe+s9xjz/Mo89+giZmZkccGADxo1/l9fGv8urb7xNeno6J5zUEYCRIx6l\n39X9GTf+XfpddS2PDy8eDmOQu/lx5O1nZW8b++E0ul37VI7j3tznZCZNsxzWaxDfzviHgZd0LPC+\nJENB9fePucs4++bn+f7n4vXc6PjmT3+//HEOrXoPpu15Q5m7aFXx628+y+xP3X0udz7+AW3OeYQJ\nk37jpotPLvS+xWKfXPNvPoNu1z9Lq3MepXenljn1sO0MDepWp8WZQ+g/+G1G3dYTgMzMLG597ENa\nnzuM4y8fTb8z22fve0yrhpx2zMEccf5wjjx/OI+9VjwCPfbJOXpgD7pd9yytzh5K706HRxnfpjSo\nsx8tznyE/g+/w6jbegGQuTuLW0dMoPU5j3L8ZaPo1zs0vjdffCKTZvzDYb2H8O1PcxnY58QC74sa\nkQuQw+pXYf6qLSxdt53MrCATf1lKx+bhCotfF6xnk4vo+WX+empVKpu97af/1rFhW07P3/9WbWH+\n6q0EAoGC7UCKtG5YnX9XbGLxmq1k7g7y3rQFnNqyblibtZt38tv8dWRmBXPsn1E1nY6HZjD223lh\n/88KBqmUXgqAyuVKs3zdtoLrRAoc2fwA5i5aycJl68jMzOLtz2bS9fgWYW26HH8o4yaKI8CMPxZQ\nqUI6NatVjLvvPwtWMm/hqmI3vkc2P4C5C1f5rvlnuh53aFibLse3YNxH/v6WDfU3zr5DBvTgjsfe\npzhRkP0tZkObzZEH12XuotUsXL6ezN1ZvP3VLLoe0yysTZdjmjHuE4lunPHnYulzVTFEbtsh81WZ\nUiUpUSKNIPKcT/ppHsGgfJ4+eyF1aoYb8oqKw+pVYf6qraE5+tdlnHxIxBy9cD2bvTl6wXpqVy6T\nvW1mjDl6687d2Z/TS5cgK5hzvisKDj+gKv+t3MKStdvIzAoy4acldDq0dlibdVt28vuiDVHn6BJp\nAcqVLkmJtABlS5dgxYacXm5dWmXwwU/Fw+PviEY1mLdsI4tWbSFzd5C3pvxHlyPrh7VZs2kHv/y7\nhszdWTn2/2HOCtZF8T7uclR9XvtGnHxe+2YuXY+qn6NNUXBkk1rMXbaehas2y/P7/T90bdMgrE2X\nNg0Y97VkAJnx90oqlS9DzSrpAJRIS6N8mVKUSAuQXqYkS9dsBqBRRhWm/imRLZN+XcwZ7Q8qxF4V\nD/7+6w8y6tanVu0MSpYsxbEndebHyd/EbP/tV59w3MmdAahWfT8OatwUgPRy5ah3QAPWrJJIv4/e\nf4ve519KiZKSGKhylarRD1gEHNk0g7mL17FwxUa5nybNpuvRTcLadGnfhHGfzwJgxpylcj9VFUec\nQCBAWlr0xW7INR25Y0xOb+OipCDXfI9eHVsy/rOfC6U/iSio/vbt3YFHX/qC3W5OXeOi3YqagpLZ\nJ02zIflm1nzq1Ax33ClKjmyawdwl61i40nuG/6Tr0eFObV3aN2HcF5Khw/8MVyxXmqNb1GPsZ/J8\n784Ksmnrzux9Xv1c9nn1s9/perQpxF7F5shD6st9udyN0+e/0PW4iDE+rgXjJorzX9g9HWffrKwg\nlSvIOlmlYjpLVxYP55cjTW03vptkfL/9m67twtfnLm0PYtxX4qw6wy6nUrnS1KxSLqzNiS3r8++y\nDSxZLWv+QRlVmDp7KQCTfl3IGUcXD0dIGaPVMka73RgdHx7l2eW4Foz7yI3v7IXuGZZ3lKm//cf6\njTl1CP6oq/JlS5MVRf4tCo5sVoe5i9ewcMUG6e/Xf9C1Q/iz1qVDU8Z9Jo5JM/5aQqUKoTW4To1K\nnNK2MS9OzLnmFNf30MJi9u+zqF//APbPqEPJUqXo1Pk0vpkULpMEAgG2bJH1a8uWLVSuUoWSJcOT\nOE7/8Qfq1q1PrdryLpUWSGPzZolo27RpEzVrhr9TFiV5mR8Bpv76L+ujZIrrcnwLXnX7vDpxeo51\ntagoqP7+s2Al8xYVQz2djm++9HfS9L9DMt3vxUemKyiZvdEBNZj6qzjQTJr2N2ecVDwc42EfXPMP\nqReuh/3iV7oee0hYmy7HHsK4j2cCMGP2IiqVL0vNahVYsXYTs/4RuW3Ltp3Y+SvJqCH61r492/Ho\ny1+H3ss2FI+Mn/veHO2Nr3c//0rX4yLv50MY97FkFJL72Y3vmijj6/TpXY49hFc/kn1e/einHMcs\nCPZaI7Ixpqkx5lZjzEj3c6sxplniPfOP2pXLsmx9SMm+bMN2alUuG7P92W3r8e2cnN6iewr7V01n\nydrQg7x03Tb2r5qe9P4PnteKe9/8JXvh9rhr3M/cf05LfhvejfvOOoz73yoeURwZNauwePn67L8X\nr1hPRoSgkVGjMotXhKJrl6xcT0bNykntW9yI7Mti15ewNhH9WrLC9TfOvqcf25zFK9Yze27xiF70\nKKj+Alx59rH8+PotPHn3uVSqEHtOKGwyalRisU85tnjlRjJqVMrZxhc9tnRVqE0gEOCHl65l/sTb\n+XrGXGb+tSTHOS46/Qg+/+HvAupBatSqXIZl60PC5fL128OMxJGc3bYe3/6V3Bx9U+fGfH/X8XRr\nuT+PfVo8sgrUrlKWpT4nnGXrt1G7SnJz9IoN23nmq3n8+GAnZjzUiY3bdjHZhn8XRx1UjVUbd7Bg\ndfEQTjOql2PxmpABY8maLWRUKxdnj+SoUSmdlc6AvmL9NmpUSn6dK0gyqpdn8arN2X8vXr2FjOrl\nc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/KWknVfjdTeNt2fs47B2kQ83Wzj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90h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PNX6NB/uueCjCWxtWewx/zFKoI/b2f0Cb/8YbTphiWNmJfvYdX2Y9Qvn4/QWV2NNv35MgAC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\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kSZI0L0wiS5IkSdLoM9D6urXmJyKiMen6g8z8RERsAfwcWL1p278CR2TmpIEqjIj3A1+g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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12513c2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (27,81));\n",
    "\n",
    "sample_size = 50\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "regr.fit(X_train, y_train)\n",
    "fifty_score = regr.score(X_test, y_test)\n",
    "predicted = regr.predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[0], cbar = False);\n",
    "axes[0].set_ylabel('Actual label', fontsize = 45);\n",
    "\n",
    "sample_size = 100\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "regr.fit(X_train, y_train)\n",
    "hundred_score = regr.score(X_test, y_test)\n",
    "predicted = regr.predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[1], cbar = False);\n",
    "axes[1].set_xlabel('Predicted label', fontsize = 45);\n",
    "\n",
    "sample_size = 5000\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "regr.fit(X_train, y_train)\n",
    "five_thousand_score = regr.score(X_test, y_test)\n",
    "predicted = regr.predict(X_test)\n",
    "cm = metrics.confusion_matrix(y_test, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True, cmap = 'Blues_r', ax = axes[2], cbar = False);\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "fifty = '50 Training Samples Score: {0}'.format(fifty_score) \n",
    "hundred = '100 Training Samples Score: {0}'.format(hundred_score) \n",
    "thousand = '5000 Training Samples Score: {0}'.format(five_thousand_score) \n",
    "axes[0].set_title(fifty, size = 30);\n",
    "axes[1].set_title(hundred, size = 30);\n",
    "axes[2].set_title(thousand, size = 30);\n",
    "fig.suptitle('Confusion Matrices', y=0.568, size = 55);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align='Left'>All Training Samples</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 34s, sys: 809 ms, total: 8min 34s\n",
      "Wall time: 8min 35s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.89590000000000003"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr2 = LogisticRegression(solver='sag')\n",
    "sample_size = len(train_dataset)\n",
    "X_train = train_dataset[:sample_size].reshape(sample_size, 784)\n",
    "y_train = train_labels[:sample_size]\n",
    "%time regr2.fit(X_train, y_train)\n",
    "regr2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3220kEgmYTCbo9Xq4XK6ZtOMhNBoNpNNpnJ+fs7onFDu4bYxKksRWn9LO8lTX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\nJCQkFhSSwCUkJCQWFP8Hy4VnLhTZWS0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c853450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_labels = regr2.predict(X_test)\n",
    "disp_sample_dataset(test_dataset, pred_labels)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "1_notmnist.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
